techniques for performance improvement of cognitive...
TRANSCRIPT
i
Techniques for Performance Improvement of
Cognitive Radio
Aamir Zeb Shaikh
Thesis submitted for the
Degree of Doctor of Philosophy
Department of Telecommunications Engineering
NED UNIVERSITY OF ENGINEERING & TECHNOLOGY
University Road
Karachi 75270
2015
ii
Techniques for Performance Improvement of
Cognitive Radio
By
Aamir Zeb Shaikh
Project Supervisor:
Prof. Dr. Syed Shoaib Hassan Zaidi
Project Co-Supervisor:
Prof. Dr. Talat Altaf
Department of Telecommunications Engineering
NED UNIVERSITY OF ENGINEERING & TECHNOLOGY
University Road
Karachi 75270
2015
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Abstract
Cognitive Radio is a promising technology to resolve spectrum scarcity issue by exploiting
RF spectrum in opportunistic fashion. Spectrum Sensing is a key enabling step towards suc-
cessful implementation of this emerging technology. Sensing refers to the detection of unused
spectrum spaces also known as “white spaces”. This dissertation proposes, investigates and
analyses several algorithms for spectrum sensing cognitive radio applications. Receiver Oper-
ating Characteristic (ROC) is compared among different proposed algorithms. It is one of the
most important measures to classify detectors. In the first part of the dissertation, ROC is ana-
lysed for multiple-antenna assisted spectrum sensing radios under shadowing and is derived
using linear test statistic. Furthermore, a highly useful cluster-driven architecture for spectrum
sensing is also proposed and analysed that improves detection probability by exploiting coop-
eration among cognitive radios using hard decision combining strategy. Hard decision
combination strategy computes the detection probability by combining one bit decisions
among various cooperative cognitive radios. Detection probability is achieved 80% at P FA rate
of 10% for a single user, whereas using hard decision combing approach the same detection
probability is achieved at 1% PFA. For Binary Symmetric Channel with 10-3
error probability,
PD results 32% (at PFA 10-2
) for a single user whereas 65% for a five user case. In the second
part of the dissertation, a novel channel model i.e. double exponential correlation is incorpo-
rated for spectrum sensing algorithms under suburban environments. Asymptotic probability
of detection is derived, analysed and compared with classical exponential correlation model
(also known as Gudmundson's model). Using proposed model missed detection probability
reaches Zero for less than ten sensors whereas Gudmundson‟s model results a constant 0.7
missed detection probability even when the sensing users are increased to hundred. Thus, re-
sults verify that the proposed model performs significantly better than the classical one. In the
third part of the dissertation, a cooperative sensing strategy is proposed for mobility-driven
cognitive radio. It is also verified through simulation results that the proposed decision-fusion
based architecture performs significantly better than the independent sensing radios. Using
collaboration under urban environments, missed detection results in 30% in comparison to
62% for a single user under false alarm probability of 10%.
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Acknowledgements
I won four international scholarships for pursuing PhD studies at various universities of the
world. I selected Monash University, Malaysia campus and requested the then vice chancellor
of the university, Engr. Abul Kalam for study leave. He motivated me to pursue PhD studies at
NED University. My highest regards for him, may Allah rest his soul in peace.
I would thank like to thank to my PhD advisors, Professor Dr. Shoaib Hassan Zaidi and
Professor Dr. Talat Altaf, for supporting me during the course of PhD at this university.
Through the energetic and motivational behaviour of Dr. Shoaib Zaidi, I learnt how to pursue
various tasks in parallel. Dr. Talat Altaf provided guidance in a kind and a truly professional
manner. His patient listening and always ready to help attitude towards any of the research
issues helped me to complete many difficult tasks in time. Dr. Lakshman Tamil at the Univer-
sity of Texas at Dallas, TX, USA was my faculty supervisor during research visit to USA. His
insightful discussions helped me to resolve many research problems. He provided me guid-
ance, necessary to complete the research in due time. Dr. Mohammad Saquib, professor at
UTDallas, TX is also highly appreciated who provided me an opportunity to work at the Uni-
versity of Texas, Dallas. Moreover, we also wrote a joint research proposal for grant
application submitted to Pak-US strategic research collaboration.
Encouragement, cooperation and assistance put forward by Dr. Muhammad Imran Aslam,
Co-Chair at Telecommunications, NED UET and Dr. Ghous Bux Narejo, Ag. Chair in Elec-
tronic Engineering Dept. at NED University is also highly commendable.
I am also thankful to Dr. Yunfie Chen, associate professor at Warwick University, UK. We
wrote a joint research proposal that was submitted to UK government.
Dr. Imran Tasadduq, associate professor at Umm ul Qura, KSA, Dr. Shahid Sheikh, associ-
ate professor at Habib university, KHI, Dr. John Fonseka, professor at Telecommunications
Engg. UTD, Dr. Gul Agha, professor and director at Open systems laboratory at UIUC, USA,
Dr. Mohammad Mehdy Masud at UAE University, UAE, Rakibul Hassan, Zafar Ahmed Sade-
que and Dr. Tariq Ali at UTDallas,TX, Dr. Sagheer Shaikh at Broadcom Corp. CA, Dr. Syed
Ali Askari at Intel Inc., Dr. Faisal Kashif, faculty at MIT, USA and Mr. Syed Abdullah at
Blackberry inc. Dallas, TX provided me help at different levels during my PhD journey.
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Table of Contents
Abstract ...................................................................................................................... iii
Acknowledgements .....................................................................................................iv
Table of Contents......................................................................................................... v
List of Abbreviations ...................................................................................................ix
List of Figures ............................................................................................................. x
List of Tables...............................................................................................................xi
1 Introduction............................................................................................................ 1
1.1 Motivation....................................................................................................... 1
1.2 Cognitive Radio............................................................................................... 3
1.2.1 Spectrum Sensing........................................................................................ 4
1.2.2 Spectrum Management ................................................................................ 7
1.2.3 Spectrum Sharing........................................................................................ 7
1.2.4 Spectrum Mobility ...................................................................................... 8
1.3 Importance of Cognitive Radio Technology in Pakistan ...................................... 8
1.4 Applications of Cognitive Radio ......................................................................13
1.4.1 Cellular Communication .............................................................................14
1.4.2 Vehicular Safety .........................................................................................14
1.4.3 Wireless Medical Networks ........................................................................15
1.4.4 Smart Grid Networks..................................................................................16
1.4.5 Disaster Management & Emergency Networks.............................................16
1.5 Contributions..................................................................................................18
1.6 Dissertation Organization ................................................................................19
2 Spectrum Sensing Techniques for Cognitive Radio Communications.........................21
2.1 Non Cooperative Spectrum Sensing Algorithms ................................................24
2.1.1 Matched-Filter Detector..............................................................................24
2.1.2 Cyclostationary Detector ............................................................................24
2.1.3 Energy Detection based Spectrum Sensing...................................................25
2.2 Cooperative Strategies.....................................................................................27
2.2.1 Data Fusion ...............................................................................................28
2.2.2 Decision fusion ..........................................................................................29
2.2.3 AND Rule .................................................................................................30
2.3 Energy Efficiency ...........................................................................................31
2.3.1 Sequential Testing ......................................................................................31
2.3.2 Censoring ..................................................................................................32
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2.3.3 Clustering ..................................................................................................32
2.4 Cognitive Capabilities in Existing IEEE standards ............................................33
2.4.1 IEEE 802.11 k............................................................................................34
2.4.2 Bluetooth...................................................................................................34
2.4.3 IEEE 802.22 ..............................................................................................35
2.5 Challenges......................................................................................................35
2.5.1 Correlated Multiple Antenna assisted Spectrum Sensing ...............................35
2.5.2 Collaborative Spectrum Sensing under Suburban Environments ....................36
2.5.3 Mobility driven Cognitive Radios................................................................37
3 Multiple Antenna Assisted Spectrum Sensing under Correlated Antenna Elements .....38
3.1 Introduction....................................................................................................38
3.1.1 Contributions .............................................................................................39
3.1.2 Organisation ..............................................................................................39
3.2 Related Work ..................................................................................................39
3.3 System Model.................................................................................................40
3.3.1 Computation of Receiver Operating Characteristic (ROC) for Local Sensors.41
3.3.2 Multi-Antenna aided Sensing ......................................................................42
3.3.3 Hypothesis Testing .....................................................................................43
3.4 Decision Computation on Fusion Centre...........................................................45
3.5 Simulation Results ..........................................................................................46
3.6 Conclusion .....................................................................................................49
4 Collaborative Spectrum Sensing under Suburban Environments................................51
4.1 Introduction....................................................................................................51
4.1.1 Contributions .............................................................................................52
4.1.2 Organisation ..............................................................................................52
4.2 Related Work ..................................................................................................52
4.3 Spectrum Sensing Model .................................................................................54
4.4 Simulation Results and Discussion ...................................................................56
4.5 Conclusion .....................................................................................................58
5 Cooperative Spectrum Sensing under Mobility Driven Cognitive Radios ...................59
5.1 Introduction....................................................................................................59
5.1.1 Contributions .............................................................................................61
5.1.2 Organisation ..............................................................................................61
5.2 Related Work ..................................................................................................61
5.3 Proposed System Model ..................................................................................62
5.4 Numerical Results & Discussion ......................................................................64
5.5 Conclusion .....................................................................................................66
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6 Conclusion & Future Issues ....................................................................................67
6.1 Conclusion .....................................................................................................67
6.2 Future Issues ..................................................................................................69
6.2.1 Correlated Narrowband Noise .....................................................................69
6.2.2 Interference Intrusion .................................................................................70
6.2.3 Wideband Sensing......................................................................................70
6.2.4 Complexity ................................................................................................71
6.2.5 Quickest Detection .....................................................................................71
6.2.6 Hidden-Node in Wireless Communications ..................................................72
References .................................................................................................................73
Vita............................................................................................................................89
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List of Abbreviations
AWGN Additive White Gaussian Noise
BSC Binary Symmetric Channel
CLT Central Limit Theorem
CR Cognitive Radio
DSA Dynamic Spectrum Access
DSL Digital Subscriber Line
DSP Digital Signal Processing
ED Energy Detection
EvDO Evolution Data Optimized
FCC Federal Communications Commission
FCC Federal Communications Commission
FDI Foreign Direct Investment
FTTH fibre to the Home
GNU GNU‟s Not Unix
HFC Hybrid Fibre Coaxial
LU Legitimate User or Licensed User
NTIA National Telecommunications and Information Administration
Pd Probability of Detection
Pe Probability of Error
Pfa Probability of False Alarm
Pmd Probability of Missed Detection
PSD Power Spectral Density
PTA Pakistan Telecommunications Authority
PU Primary User
PU Primary User
ROC Receiver Operating Characteristic
ROC Region of Convergence
SNR Signal to Noise Ratio
SU Secondary User
Wimax Worldwide Interoperability for Microwave Access
ix
List of Figures
Figure 1.1 Cognitive Cycle [13] ......................................................................................... 4
Figure 1.2 Detection of Primary Receivers through Local Oscillator Leakage Power ............. 6
Figure 1.3 Interference due to Hidden-node......................................................................... 7
Figure 1.4 Comparison of Mobile Teledensity and Total Teledensity in Pakistan ................... 9
Figure 1.5 Portion of Cellular in Total Telecommunication Invetments in Pakistan ...............10
Figure 1.6 Percentage of Telecommunication Share in FDI ................................................10
Figure 1.7 Comparison of Telecommunication FDI with total in Pakistan ........................... 11
Figure 1.8 Impact on Revenue due to Telecommunication Business ....................................12
Figure 1.9 Comparison of Broadband Technologies in Pakistan ..........................................13
Figure 2.1 An Overview of Spectrum Sensing Algorithms ...................................................23
Figure 2.2 Relaying in Spectrum Sensing Cognitive Radio ..................................................23
Figure 2.3 Cooperation among Cognitive Sensors..............................................................28
Figure 3.1 Proposed Architecture for Correlated Energy Detector with Cooperation ............42
Figure 3.2 False-Alarm Probability relation with Received Power .......................................47
Figure 3.3 ROC Performance curves under OR based Hard Decision Combining Rule ..........48
Figure 3.4 Compares Hard Decision Combining Rules under BSC channel with Pe=10-3
......49
Figure 4.1 Cognitive radio network, sensing TV Transmitter ...............................................55
Figure 4.2 Relation between Missed Opportunity and No. of Secondary Users .....................57
Figure 5.1 Proposed Architecture for Mobility-Driven Cognitive Radio .............................60
Figure 5.2 ROC Comparison under Suburban Environment ................................................64
Figure 5.3 Cooperation under Urban Environments ............................................................65
Figure 5.4 Cooperation under Suburban Environments.......................................................66
x
List of Tables
Table 1.1 Mean Spectral Occupancy Results in New Zealand [7] ......................................... 3
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1 Introduction
In this research work, novel spectrum sensing algorithms are proposed, derived and ana-
lysed for successful implementation of cognitive radio (CR). These algorithms improve
performance of spectrum sensing cognitive radios. These techniques include a multiple-
antenna assisted spectrum sensing scheme for correlated-sensing elements, collaborative spec-
trum sensing under suburban environments using double correlation model and a cooperative
sensing scheme for vehicular cognitive radios. This chapter presents advantages of cognitive
radio technology, brief summary of evolving technology along with its functional units and
contributions of the dissertation. The chapter ends with a brief summary of our research con-
tributions and dissertation organization.
1.1 Motivation
Wireless technology is growing rapidly at a tremendously increasing rate. The development
of novel wireless technologies and protocols for next generation wireless networks and stand-
ards require bandwidth. Moreover, the increased bandwidth requirements of end users are also
showing rapid transition from voice-only to multimedia based services. The bandwidth is re-
quired especially under 3 GHz band due to the excellent propagation characteristics [1].
However, the spectrum allocation chart developed by the National Telecommunications and
Information Administration (NTIA), USA shows that the frequency bands are already allocat-
ed in a grid-lock fashion. Consequently, it is difficult to find unused spectrum bands especially
in the desired bands i.e. under 3 GHz. On the other hand, spectrum utilization report prepared
by Berkeley Wireless Research Centre (BWRC) reveals that the large amount of bandwidth
remains mostly unutilized or under-utilized [2]. Similar studies were carried out by Federal
Communications Commission (FCC) [3], Shared Spectrum Company (SSC) [4] and others.
FCC also presented its findings through Spectrum Task Force that the average occupancy in
frequency bands from 30 MHz to 3 GHz over many locations is less than 6% with maximum
2
spectrum occupancy in New York city which is 13%[5]. Furthermore, Mehdavi et al.
conducted a study in Hull, UK [6] and Chiang et al. in Newzealand, with similar results.
Chaing et al. conducted experimenatal study to estimate mean spectral occupancy under two
environments i.e. indoor and outdoor [7]. Indoor environment was selected to be an office in
third floor of a 9-storey building whereas outside environment was selected as the top floor of
the same building. The results, as shown in Table 1.1, clearly indicate that the culprit behind
spectrum saturation issue is fixed spectrum allocation. Thus, the spectrum-scarcity issue can
be resolved to a possible extent by exploiting spectrum in an opportunistic fashion. For
efficient utilization of spectrum in secondary fashion, knowledge of RF environment is
necessary. The knowledge about unused spectral bands can be accomplished through two
different architectures. In first type, the information about spectral holes is broadcast
externally [8] and the second architetcture involves sensing the RF spectrum [9]. The focus of
this dissertation is the development of spectrum sensing algorithms that result in improved
performance metric i.e. Receiver Operating Characteristic (ROC) [10, 11]. These algorithms
have been applied to both static environments as well as vehicular environments.
Additionally, a novel fading model i.e. double correlation is also employed in spectrum
sensing domain, in this research work.
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Table 1.1 Mean Spectral Occupancy Results in New Zealand [7]
1.2 Cognitive Radio
Cognitive radio is an intelligent wireless communication device that combines both intelli-
gent signal processing techniques and software defined radio technology. Joseph Mitola,
currently IEEE fellow and Vice President for Research Enterprise at Stevens Institute of
Technology, NJ, USA and previously a scientist at MITRE ( A non-profit organisation based
in USA that operates multiple federally funded research and development centres) , coined the
term “cognitive radio” and defines as “the point in which wireless personal digital assistants
(PDAs) and the related networks are computationally intelligent about radio resources related
to computer-to-computer resources and wireless services most appropriate to those needs”
[12]. Thus, a full cognitive radio will combine radio flexibility, intelligence and spectral
awareness along with analysis behaviour of its environment.
Cognitive radio is similar to any wireless communication device which uses intelligent sig-
nal processing techniques to monitor spectrum bands for spectrum holes and exploits those by
modifying its transmission parameters for efficient usage of spectrum without creating harmful
interference for licensed or co-users. For successful realization of this novel technology, cog-
nitive radio follows following functional units also called cognitive cycle [13], as shown in
Figure 1.1
Allocation Type Outdoor
(%)
Indoor
(%)
Fixed Linking Service (FS) 6.67 4.49
Land Mobile Radio (LMR) 8.95 4.41
Cellular Mobile Service (MS) 33.37 25.93
Aeronautical Radio 2.35 8.47
Other 3.83 1.86
Overall 6.21 5.72
4
Figure 1.1 Cognitive Cycle [13]
1.2.1 Spectrum Sensing
Cognitive radios are also known as secondary radios because these radios exploit licensed
spectrum in secondary fashion. For the successful completion of cognitive cycle [13], cogni-
tive radios detect the unused spectrum spaces through spectrum sensing to exploit those in
opportunistic fashion. Spectrum sensing can be classified into primary transmitter and prima-
ry receiver detection algorithms.
Primary transmitter detection techniques refer to the algorithms in which cognitive radios
detect the presence of a legacy/licensed transmitter. These techniques include energy detec-
tion, matched filter detection and Cyclostationary feature detection [14-17]. Matched filter is
an optimal detector under additive white Gaussian noise (AWGN) for sensing known primary
transmitter signals [18]. Thus, in absence of the knowledge of signal parameters the required
gains cannot be achieved. Energy detection based sensing produces optimal results in case of
insufficient knowledge about transmitter characteristics such as modulation type, channel
gains etc. However, these detectors are vulnerable to the noise uncertainties [18]. Furthermore,
energy sensors cannot differentiate between primary user signal, interference and noise. Thus,
under low SNR regime, the performance of energy detectors is highly compromised due to
higher probability of false alarm (P fa). Cyclostationary feature detectors exploit the built-in
5
property of the modulated signals i.e. periodicity. These schemes are robust to the noise uncer-
tainties as compared to energy detectors; however, it is achieved at additional computational-
complexity. Moreover, these algorithms are also capable of differentiating among different
transmitter signals types.
Reliability of local sensing schemes is constrained due to low SNR and hidden node [19].
In such situations, cooperative sensing algorithms are recommended for the detection of pri-
mary transmitter signals. These algorithms exploit cooperation among cognitive sensors for
computing efficient and reliable decisions regarding presence or absence of primary users un-
der low SNR regime with hidden node issues [20]. The cooperation may be exploited through
hard decision fusion and soft decision combination. In wireless communications, hidden-node
problem occurs when a communication node is visible to an access point but invisible to other
communication devices, communicating with access point, as shown in Figure 1.3. This leads
to harmful interference to the communication devices already connected with access point.
Cooperative communications is a paradigm that improves the performance of wireless net-
works through increasing capacity and multiplexing gain [21-23]. Moreover, cooperation also
provides improved communication under severe fading by exploiting spatial diversity [23, 24].
6
Figure 1.2 Detection of Primary Receivers through Local Oscillator Leakage Power
Primary receiver detection is one of the most efficient types of spectrum sensing technique
that detects the spectrum holes by sensing primary receivers. Local oscillator power emitted
by the RF circuits is exploited for detection purpose. This technique was proposed by [25], for
sensing television receivers. However, it is not being used extensively due to high implemen-
tation cost and complexities. The architecture is shown in Figure 1.2
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Figure 1.3 Interference due to Hidden-node
1.2.2 Spectrum Management
Spectrum management or spectrum decision refers to the techniques by which spectrum
holes are allocated to different secondary (low priority) users according to their specific re-
quirements to satisfy Quality of Service (QoS) requirements [26]. This step involves spectrum
analysis and decision as its functional units. This unit involves not only the statistical charac-
teristics of the cognitive users but also the knowledge of primary user transmission
characteristics is necessary for efficient allocation of spectrum holes to secondary devices [27-
32].
1.2.3 Spectrum Sharing
Spectrum Sharing refers to the shared nature of wireless medium. It includes most of the
functions of medium access control (MAC) protocol [33, 34]. Moreover, cognitive radios are
capable of coexisting with primary users as well as secondary users. The four aspects related
with spectrum sharing are the architecture, spectrum allocation behaviour, access schemes and
scope [14]. Architecture can be classified as centralized or distributed. In centralized architec-
ture, the spectrum allocation procedure is managed by a centralized entity. On the other hand,
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in distributed architecture these decisions are taken by the node itself in a distributed fashion.
The second aspect is the allocation behaviour. This can be classified into cooperative and non-
cooperative spectrum sharing. Access technology provides another scheme of classification i.e.
overlay and underlay network access. In overlay schemes, cognitive radios access the RF spec-
trum in case it is not being used by the primary users while in underlay approach the spectrum
is exploited by cognitive radio users in such a fashion that they co-exist with the primary users
(PU) such that the cognitive user is considered by the primary users as the noise. Underlay
sensing algorithms utilize wireless resources more efficiently, however, at a higher implemen-
tation cost [26]. Finally, the spectrum sharing under a cognitive network can be intra-network
spectrum sharing and among multiple co-existing CR networks (internetwork sharing) [33-
36].
1.2.4 Spectrum Mobility
Seamless connectivity of secondary device even in the case of a reappearing primary user
is achieved by the functional unit of spectrum mobility [26, 37, 38]. This feature of the cogni-
tive radio devices gives a new type of handoff i.e. spectrum handoff. Thus, after the cognitive
user changes the transmission frequency of the upper layers must also change themselves for
efficient operation. Thus, this unit specifies the characteristic features of the cognitive radio
users so that they can transfer bands smoothly [37-40].
1.3 Importance of Cognitive Radio Technology in Pakistan
Pakistan is the sixth-most populated country in the world with more than 190 million peo-
ple. In 2003, Pakistan had a mere 4.31% tele-density (number of telephones available per 100
people) as compared to her neighbours, India 7.1% and Sri Lanka 12.2%. It rose to 75.77% by
September 2013, slightly lower than India‟s 79% as shown in Figure 1.4 [41-44]. The tremen-
dous increase in tele-density is mainly been due to the mobile telecommunication industry
which resulted in increasing the country‟s Foreign Direct Investment (FDI) from a tiny US$
9
10.7 million in 1976-77 to a substantial US$ 5.14 billion in 2006-07 and US$ 1.5 billion in
2010-11. Its 54.15% share in FDI during 2005-06 surpassed the contribution of the Oil and
Gas industry, thus contributing 2% to the GDP [41]. However, current decrease in FDI due to
telecommunication sector, as shown in Figure 1.5 and Figure 1.6, is due to poor law and order
conditions in the country, which are improving day by day.
Figure 1.4 Comparison of Mobile Teledensity and Total Teledensity in Pakistan
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Figure 1.5 Portion of Cellular in Total Telecommunication Invetments in Pakistan
Figure 1.6 Percentage of Telecommunication Share in FDI
11
Figure 1.7 Comparison of Telecommunication FDI with total in Pakistan
The Figure 1.7 shows the significant impact of Foreign Direct Investment (FDI) due to tel-
ecommunications industry in comparison to total [41].
Figure 1.8 indicates the impact of telecommunication business on Pakistani exchequer. Dur-
ing 2011-12, total PTA deposits stood at PKR.133.4 billion in comparison to mere 38.4 billion
in 2003-04. This amount includes general sales tax, activation tax and other contributions[41].
12
Figure 1.8 Impact on Revenue due to Telecommunication Business
Figure 1.9 compares the number of internet users through different broadband schemes in
Pakistan. Wimax (Worldwide Interoperability for Microwave Access, a wireless broadband
technology for 4G), DSL (Digital Subscriber Line, a technology to transmit digital data over
telephone wires) and EvDO (Evolution-Data Optimized is a wireless broadband protocol) us-
ers grew multi-fold over the past 6 years [41]. However, HFC (Hybrid Fiber-Coaxial Network
that combines fiber and coaxial cables) users show a decrease in number of users. During
2012-13 total broadband users stand at 2.72 million in comparison to mere 26 thousand users
during 2005-06[41].
13
Figure 1.9 Comparison of Broadband Technologies in Pakistan
The growth of telecommunication industry in Pakistan over the past decade is based on the
backbone of electromagnetic spectrum. This scarce natural resource is divided into spectrum
bands which are allocated to different services such as broadcast, mobile, fixed and satellite
services. The studies suggest a strong growth in per user and aggregate data rates are expected
especially in GSM bands, over the time period 2010-2020, which will result in a critical prob-
lem in finding carrier frequencies with required bandwidths [45]. The implementation of
cognitive wireless technology will improve the GDP growth of our country.
1.4 Applications of Cognitive Radio
Cognitive Radio can be used to enhance the quality of service (QoS) as well as to extend
the coverage area of existing wireless users along with optimizing the resources for wireless
users such as RF Spectrum. Following are the key areas where these intelligent radios can
play a vital role in enhancing the capacity and improving the quality of service to users [17,
35, 46-52].
14
1.4.1 Cellular Communication
Cellular communication users are increasing rapidly. In addition to that, capacity require-
ment of these users is showing an exponential-rising trend. One of the most important reasons
behind this phenomenon is the shift from classic voice-only services to multimedia-based ser-
vices especially the widespread use of social networks such as YouTube, Facebook, Hulu etc.
and other such social networking forums. This is in addition to the email and web browsing.
This shifting trend of cellular users is also recognized by FCC‟s National Broadband Plan [48,
53].
Due to above shifting trend in the use of wireless devices and services, studies suggest the
broadband spectrum shortfall could reach 300 MHz in USA by the end of 2014. This addition-
al spectrum would create a value of $100B [54].
Presently, only a small amount of bandwidth is opened for the secondary use of the cellular
users. In future, this could increase as the national plan suggests the TV spectrum owners to
open more licensed spectrum for secondary access. This additional bandwidth can be used by
hotspots especially installed at the sports stadiums where audience records the live moments
and instantly uploads on networking websites. However, this increased amount of bandwidth
cannot be provided by Wi-Fi [55-59].
1.4.2 Vehicular Safety
Intelligent Transportation System is an important field of interest especially in metropolitan
areas of the countries such as New York in USA, London in UK, Melbourne in Australia,
Mumbai in India and Karachi in Pakistan. The major problem arises in peak operation hours
especially in morning and evening i.e. commuters travelling from home to office and vice ver-
sa. Thus, in such situations, the traffic control system of the associated territory can inform the
commuters about traffic congestion, an estimated traffic intensity on subsequent timing and
probable alternate routes for traffic diversion [16, 60, 61]. Cognitive radios can be applied on
traffic signals for successful realization of the proposed concept i.e. efficient management of
15
transportation system. Cognitive radios can estimate the traffic through advanced estimation
techniques, predict the future traffic on the basis of statistical and historical data of the con-
cerned routes and advise the alternate routes also. Thus, cognitive radios can help in managing
the metropolitan-traffic efficiently which will result in saving millions of dollars.
Many road accidents occur due to non-visibility of the vehicles on the road. Thus, a single
road accident may result in series of accidents like a chain-reaction, which results in loss of
valuable lives and property worth millions of dollars [62]. This loss can be saved up to a large
extent by providing timely information about road accidents and valuable travelling infor-
mation to the concerned travellers. Cognitive radio is an efficient method of providing
communication means via exploiting whitespaces in an opportunistic manner. Thus, invaluable
lives and property can be saved [63-66].
1.4.3 Wireless Medical Networks
Recently, there is growing interest in wearable and implantable low power sensors that
measure critical parameters of a human body. These include temperature, pressure, humidity,
blood sugar, electrocardiogram (ECG) etc. to timely provide medical assistance [67, 68]. This
will not only improve the healthcare conditions of patients but will also help in reducing the
health-care bill of the country. Typical telemedicine systems consist of three tiers. First tier
comprises of sensors that use short range communication technologies such as IEEE 802.14.5
(Zigbee), IEEE 802.15.1 (Bluetooth) for wireless sensing purpose. Second tier consists of a
controller or processor that collects the raw data from sensing nodes, processes the data and
transmits to medical staff at a hospital, which represents third tier of telemedicine system. In
such scenarios, these intelligent radios can provide a reliable way of transmission of medical
data between sensors and the hospital by exploiting the spectrum in secondary manner. Fur-
thermore, 2360-2400 MHz band is also allocated for the secondary access of the medical
sensing devices. As many commercial off the shelf devices are using IEEE 802.15.4 in the
bands adjacent to 2400 MHZ. The implementation of proposed concept will be realized with-
out significant modifications into existing hardware [69-73].
16
1.4.4 Smart Grid Networks
Energy crisis is one of the major issues in developing as well as developed countries. Smart
grid technology provides an efficient way of transmission of electricity to the end users by ex-
ploiting the use of information and communication technology. Smart grid network typically
consists of three layers that include: physical power layer, communication networking layer
and application layer [74, 75]. The first layer is also known as Home/building area network
(HAN) which connects the smart meters. Second layer comprises of the equipment that con-
nects the first layer with the controlling/data centre. This layer is also known as Field Area
Network. Third layer consists of the controlling centre and its associated facilities. On this
layer, optical fibre based communication link can be utilized as well. Cognitive radio can be
operated in opportunistic fashion for data-transmission between control centre and the custom-
er equipment i.e. first layer and the third layer [76-79].
1.4.5 Disaster Management & Emergency Networks
Cognitive radio is a valuable technique to provide communication links for emergency sit-
uations such as disaster – hit areas. These intelligent radios can provide seamless connectivity
to damaged areas in emergency conditions without the need of further infrastructure, which is
essentially required in the urgent situations [48, 51, 80-82]. In a disaster-hit area, the existing
communication networks such as cellular/WLAN could go out of order due to the infrastruc-
ture damage. Furthermore, due to higher use of safety spectrum the communication network
goes overcrowded. Cognitive radio is capable of not only exploiting the unused cellular spec-
trum in secondary fashion but also can use WLAN (Wireless LAN), unused TV and
microphone white spaces efficiently. And this is also achieved without any complexity due to
its inherent capability of integrating secondary network with primary network on non-
interfering basis. These natural calamities can result in two problems i.e. impaired communi-
cation infrastructure and changes in wireless channel. The first direct impact of disaster is the
17
breakdown of wireless communication infrastructure due to dismantling of the buildings
which indirectly results in second issue i.e. drastic variations in wireless propagation channel.
The consequences of the first issue are the dropping of capacity or complete loss of communi-
cation. On September 11 2011, USA faced a complete collapse of communication
infrastructure including breakdown of fibre optic cables etc. In emergency conditions, such as
those under consideration, the communication can be established via other routes as well but
increased amount of traffic can lead to an overloaded network. Thus, communication system
can respond by dropping calls / denying accesses, which will result in overall system capacity
to fall further down. In such conditions, cognitive radios can exploit the knowledge of call
blocking probabilities efficiently. The intelligent signal processing capabilities of these radios
can overcome the damage to some extent. For example, when the infrastructure is completely
demolished, cognitive radios try to search other cognitive radios to establish a cognitive
adhoc-network to communicate with unreachable devices. This helps the rescue workers in
searching the victims of the disaster in time that results in saving many precious lives without
additional cost [48, 83].
On the other hand, second issue i.e. indirect impact of the infrastructure loss, change in
channel models. The extraordinary variations in wireless channels result in changing the
probability distribution functions (PDFs) of received signal [48, 83-86]. For example, previ-
ously, the signal had to traverse through specific infrastructure of the office while later; it has
to travel across the rubble (that consists of concrete, steel, smoke and dust). Though, a wireless
channel model that can incorporate all the unforeseen conditions cannot be designed, however,
loss can be minimized up to an extent. Cognitive radio can efficiently work in such condi-
tions. This radio uses a single hardware to access various air interfaces and channel models on
the fly through software download. Additionally, cognitive radio can also tune different chan-
nel parameters to best fix the existing conditions and can also fix the signal penetration
characteristics. This can be quite helpful especially in disaster-hit areas whereby the cognitive
18
radio can switch between the different air-interfaces and networks according to the service and
channel requirements [48, 87-90].
1.5 Contributions
The contributions of this dissertation can be summarized below:
In the first part, detection probability for correlated multiple antenna assisted cognitive ra-
dio is derived using linear test statistic [11]. Other authors [91] use partial-test statistic for the
same purpose that requires higher number of computations. Thus, our proposed algorithm
senses the RF spectrum for white spaces with lesser number of computations. Besides, it is
presented in [92], that the correlation among antenna elements deteriorates the sensing per-
formance of the detector. The performance deterioration is directly proportional to the amount
of correlation among sensors. However, no solution is proposed by Kim et al. to improve the
detection performance of spectrum sensor. A cost-efficient hard-decision combining based
strategy is proposed to improve the sensing performance under correlated antenna elements.
Hence, it is shown that the detection performance improves significantly by exploiting cooper-
ative gains of two or three correlated detectors. The performance of the sensing scheme is
measured under two scenarios i.e. ideal communication channel and realistic Binary Symmet-
ric Channel (BSC) with specified error probability ( Pe ) between sensing node and cluster-
head [18]. BSC is a common communication channel type used in information theory [18].
Error probability shows the error on BSC transmission link.
In the second part, asymptotic probability of detection is derived using double correlation
model under suburban environments. In this scheme, the sensing results are considered corre-
lated. The correlation among sensors is incorporated through double correlation model in
comparison to the classic exponential correlation model i.e. Gudmundson‟s model, used by
other researchers for spectrum sensing purpose. The performance of the proposed architecture
19
is also compared with the classic model and hence verified that the double exponential correla-
tion model shows improved performance when compared with classical model [10].
In the third part, a cooperative spectrum sensing architecture for vehicular cognitive radios
is investigated. Light-weight cooperation among vehicular radios is proposed, analysed and
presented. The results advocate the use of cooperation among vehicles under correlated sens-
ing results for producing improved performance.
The work presented in this dissertation resulted in following publications:
Shaikh and T. Altaf, "Performance Analysis of Correlated Multiple Antenna Spec-
trum Sensing Cognitive Radio," International Journal of Computer Applications,
vol. 50, pp. 28-31, 2012
Shaikh, Aamir Z., and Talat Altaf "Collaborative Spectrum Sensing under Subur-
ban Environments” International Journal of Advanced Computer Science and
Applications. vol. 4, pp. 62-65, 2013
Shaikh, Aamir Z., and Lakshman Tamil ,” Cognitive Radio based Telemedicine
System ”, Journal of Wireless Personal Communications February 2015
DOI:10.1007/s11277-015-2423-1 (ISI Indexed)
Shaikh, Aamir Z., and Talat Altaf,” Performance Analysis of a Cluster-based Corre-
lated Cognitive Radio Network”, Accepted for publication Journal of Research and
Practice Information Technology Vol. 47. Issue 1. (ISI Indexed)
1.6 Dissertation Organization
Following the introduction, motivation and brief summary of research work carried out
during PhD course, Chapter 2 summarizes state-of-the-art spectrum sensing algorithms that
are presently being used for detection of spectrum holes along with requirements imposed by
20
First IEEE standard for Wireless Rural Area Network (IEEE 802.22) on spectrum sensing sec-
ondary devices.
Chapter 3 investigates a linear static based multiple antenna assisted spectrum sensing al-
gorithm for correlated antenna elements under IEEE 802.22 framework. The proposed
algorithm is analysed under shadowing. Additionally, a cluster-based spectrum sensing strate-
gy for cooperative spectrum sensing under multiple-antenna assisted nodes in correlated
environment is also presented. The proposed architecture can be applied to avoid hidden-node
problems in wireless systems. The architecture considers communication channel between
sensing node and the cluster-head as ideal as well as realistic channel i.e. BSC with specified
Probability of error.
Chapter 4 presents the investigation of asymptotic detection performance for cognitive ra-
dios under suburban environments using double correlation model. The existing literature uses
Gudmundson‟s exponential correlation model for both urban and suburban environments.
However, authors [93] observe that the channel shadowing can best be modelled through
Gudmundson‟s exponential model under urban environments and double exponential correla-
tion model under suburban environments. The results in this chapter verify that the proposed
architecture shows improved detection performance in comparison to the classic model in
suburban environments.
Chapter 5 analyses the performance of a vehicular-driven spectrum sensing algorithm un-
der urban and suburban environments. ROC performance is compared under two channel
models i.e. Gudmundson‟s exponential correlation model and double correlation model. Fur-
thermore; hard decision combining strategy is also employed to improve detection
performance.
Chapter 6 concludes the dissertation. Additionally, future challenges are also presented.
21
2 Spectrum Sensing Techniques for Cognitive Radio Com-
munications
Spectrum sensing can be defined as the monitoring of RF environment for detection of un-
used spectrum holes, also known as white spaces. Cognitive radios get awareness about RF
environment through spectrum sensing algorithms or geographical location and database.
Through spectrum sensing techniques, smart radios can efficiently detect spectrum holes by
themselves while in geographical location based technique, cognitive radios receive infor-
mation about unused spectrum bands through external base stations. Sensing can be performed
through non-cooperative and cooperative spectrum sensing algorithms. In this chapter, a re-
view of spectrum sensing algorithms along with their benefits and challenges is presented.
Furthermore, the requirements set by the first IEEE standard 802.22 on Wireless Regional Ar-
ea Networks (WRAN) are also discussed [94-96].
Spectrum sensing or detection of white spaces is key step in the realization of a cognitive
radio. The sensing problem can be formulated using a binary hypothesis testing rule. Conven-
tionally, it can be implemented by computing test statistic using specific parameters of
received signal and comparing it with a set threshold. If the statistic is larger than the threshold
the cognitive radio gives decision in favour of the occupied band or presence of a primary us-
er, otherwise it decides in favour of null hypothesis or absence of a primary user signal.
Spectrum sensing can be implemented using various techniques that can be classified in non-
cooperative and cooperative spectrum sensing algorithms [9, 14, 15, 97].
In non-cooperative spectrum sensing algorithms, cognitive radio independently computes
the test statistic from received signal parameters and decides about the presence of legitimate
user or spectral hole. The commonly used algorithms include matched filter detector [98-101],
energy detector [102-107] and Cyclostationary feature detector [108-112]. A brief summary of
these algorithms is presented in next section.
22
In cooperative spectrum sensing algorithms, sensors cooperate with each other for compu-
ting the probabilities of detection and false alarm [20, 113-115]. Cooperation is an efficient
method especially for sensing under fading channels. Additionally, it can also produce energy
efficiency as well as sensing reliability. Cooperative strategies can be divided under two
groups i.e. soft decision [116-119] and hard decision computations [97, 120-123]. In soft deci-
sion computation method, local sensors send their log likelihood ratios (LLRs) to fusion
centre, which jointly combines the sensing results to declare the presence or absence of spec-
tral hole. LLR represents how many times the data lies under one hypothesis likely in
comparison to other hypothesis. Fusion Centre refers to a central point where the secondary
radios submit their sensing results for the computation of collaborative decisions. In hard de-
cision combining approach, local sensors compute the probabilities of detection and false
alarm regarding presence of global decisions and send one bit i.e. “0” or “1” to the fusion cen-
tre that combines the received decisions and announce the presence or absence of a PU by
broadcast.
Federal Communications Commission (FCC) introduced Interference Temperature (IT) as a
measurement parameter for analysis of interference [124, 125]. This parameter will enable
future smart communication devices to operate more efficiently. FCC set a maximum tolerable
interference limit into a particular band. Unlicensed users will measure the interference tem-
perature before starting communication, and if they find that the maximum tolerable limit for
that band is already reached, they will avoid transmission on that band. Thus, secondary users
will be able to avoid creating additional harmful interference. This parameter promises near-
optimal utilization of RF spectrum through dynamic access. This parameter will enable the
efficient implementation of future cognitive radio networks. A summary of sensing algo-
rithms is presented in Figure 2.1.
Relaying can also be introduced in cognitive communications to improve detection per-
formance of spectrum sensing algorithms, as shown in Figure 2.2.
23
Figure 2.1 An Overview of Spectrum Sensing Algorithms
Figure 2.2 Relaying in Spectrum Sensing Cognitive Radio
24
2.1 Non Cooperative Spectrum Sensing Algorithms
In this section, a brief summary of widely used spectrum sensing algorithms is presented.
These include matched filter detector, Cyclostationary feature detector and energy detector.
2.1.1 Matched-Filter Detector
Matched filter is an optimal detector [18] for detection of known Primary User (PU) signal.
Test statistic for the spectrum sensor is computed and compared with the threshold using
Equation (2.1). This detector requires short time to compute values of detection and false-
alarm probabilities; however, it is achieved at the cost of perfect a prior knowledge of primary
user signals because it demodulates the received signals before taking any decisions. Cognitive
radios exploit spectrum in an opportunistic fashion, thus, they may operate in licensed as well
as unlicensed bands. Thus, it is computationally inefficient to demodulate received signals of
all primary and coexisting secondary users before deciding in favour or against of white spac-
es or spectral holes [2, 9].
1
( ) ( ) ( )
0
NT x x n s n
n
(2.1)
In above equation, T(x) represents the test statistic, x(n) represents the primary transmit-
ted signal, s(n) represents received primary user signal, N is the number of samples, while
is the threshold that can be set by using Neyman Pearson Lemma i.e. faP . β is the
fixed value of false alarm probability.
2.1.2 Cyclostationary Detector
Cyclostationary feature detector (CFD) exploits the inherent periodicity of modulated sig-
nals to decide presence or absence of a primary user. This is due to the Cyclostationary-
statistics of primary signals in comparison to the stationary-statistics of white noise. Cyclosta-
tionary refers to the periodic-stationary characteristic. The message signals are accompanied
25
with sinusoidal carriers, spreading codes, hopping sequences, preambles, mid-ambles etc. for
synchronization or estimation purpose. These signals have in-built property of cyclostationari-
ty [126-129]. These detectors are implemented by computing spectral correlation density
function of received signals as described in Equation (2.2). Moreover, these detectors provide
an additional benefit of discriminating between useful signal, noise and interference (which is
not possible in energy detectors). The discrimination point between primary signals and the
noise is that noise is white (with stationary statistics) while primary signals have in-built peri-
odicities. This feature can be highly useful in distinguishing the type of transmitted signals in
unlicensed ISM band also, as different signals have their specific features and signatures.
Thus, a cognitive radio can efficiently operate under unlicensed bands with the aid of feature
detectors. Furthermore, these detectors show robustness to the noise power uncertainties which
is not possible in energy detection based algorithms. However, these excellent features are
achieved at the cost of computational complexity and longer sensing time. Cyclostationary
signals are not stationary but their autocorrelation functions are cyclic or periodic, whereas
noise does not have periodic autocorrelation function [127]. Spectral Correlation Function is
given by:
2( , ) ( )
j fS f R ey
(2.2)
and
2[ ( ) ( ) ]
j nR E y n y n ey
(2.3)
The above equation shows if y is Cyclostationary, then for a given value of τ , the autocor-
relation function yR
is periodic with respect to n with period T , is the cyclic frequency.
( , )S f denotes spectral correlation function.
2.1.3 Energy Detection based Spectrum Sensing
Energy detection based sensing is a preferred method for cognitive radio applications espe-
cially due to lower computational and implementation capabilities [107, 130-132].
26
Furthermore, the computation of test statistic does not require any prior knowledge of PU sig-
nals. Energy of the received signal is computed and compared with a pre-set threshold. The
threshold setting depends on the noise floor, resulting in degraded performance due to noise
uncertainties [133]. Furthermore, due to inherent blindness, detectors cannot discriminate
among PU signal, noise and interference. Besides, these detectors do not efficiently work for
the detection of spread spectrum signals [134]; this is in addition to compromised performance
under low SNR [133].
2( )
0
NT y n
n
(2.4)
22
2 0
2 22
2 1
;2
;2
wN
w sN
H
T
H
(2.5)
Consider a received signal which is modelled as a zero-mean Gaussian random variable
2( ) (0, ) ss n and the noise is modelled as
2( ) (0, )w n w . Thus, test statistic T can be
derived as above.
An experimental investigation shows that the performance of energy detectors varies with
the change of modulation schemes as well as modulation order [135]. This work considers on-
ly specular (LOS) components for detection of signals.
A novel improved energy detection algorithm for spectrum sensing purpose is proposed by
authors in [136]. The difference between the proposed detector and conventional is that the
variable p may assume any arbitrary positive value while the conventional detector has a
fixed value i.e. 2, as shown in Equation (2.6)
p
i iX y
(2.6)
27
Where iX represents the decision statistic as in Equation 2.5 which follows chi-square
distribution with 2N degrees of freedom.The degrees of freedom represents the number of
values in the computation of statistic that are free to vary.
2.2 Cooperative Strategies
Multiple cognitive radios can cooperate with each other to improve sensing performance and
reliability. Furthermore, due to sensitivity limitations of a single sensing radio [137], detection
performance is often compromised. Cooperation is also an important consideration for opera-
tion under fading channels (channel between primary users to secondary sensor). Thereby, it is
not possible for an independent sensor to decide regarding presence of PU alone. Communica-
tion under fading channels may produce hidden-node problem [1, 138-140], resulting in
harmful interference to primary users. Under such conditions, secondary radios can cooperate
with each other to produce reliable sensing results with lesser sensitivity requirements. Coop-
eration among radios can be grouped under two types i.e. data fusion and decision fusion [9,
130, 141]. Data fusion techniques are also known as soft computing while decision fusion is
also known as hard combining technique. In data fusion schemes the sensing radios send log-
likelihood ratios of local sensing to a common point (fusion centre) for computation of joint
probabilities of detection and false alarm. This central point or fusion centre then announces
the presence or absence a primary user to all the secondary radios in a particular cell or cluster.
In decision fusion strategy, cognitive radios compute the test statistic by themselves and de-
cide about the presence or absence of a PU by themselves and send one bit decisions i.e. “0”
for absence or “1” for presence of PU. These decisions are sent to the fusion centre which
computes the global probabilities of detection and false alarm and announce regarding the de-
cision by combining the decisions of the several sensors. The communication channel
between sensors and the fusion centre can be considered as an ideal or realistic i.e. BSC with
appropriate eP . The local values of probabilities of detection, missed detection and false alarm
28
are represented bydp , mdp , fap while the global probabilities of detection, missed detection and
false alarm are denoted by dP , mdP and faP .
Furthermore, cooperative sensing schemes can be further classified into centralized or de-
centralized detection rules. In centralized detection rule, sensors send their soft computed
results or one bit decisions regarding presence or absence of a PU to a fusion centre that com-
bines and announces the global results. In distributed cooperative sensing scenario, cognitive
sensors can group themselves with required properties and compute ROC decisions using co-
operation. An example of cooperative detection for cognitive radio applications is shown in
Figure 2.3.
Figure 2.3 Cooperation among Cognitive Sensors
2.2.1 Data Fusion
In data fusion algorithms, local radios sense the RF spectrum for the presence of a spectral
hole and send their sensing information in form of LLRs to the fusion centre, which jointly
combines the results and computes the global probabilities of detection and false alarm.
29
Consider an example [142] where it is assumed that noise vectors nl and source signal
vectors are assumed to be independent with distribution nl ~ ( ) 2
l0,σ I and xl ~ ( ) 2
l0,γ I
respectively. Log-likelihood ratio for this problem can be derived as:
1
2
2
l
2 2 2
l l l
y γl
σ (σ + γ )
L
l
(2.7)
In above equation, 2
l
2y
l
σ is derived using soft test statistic, 2
lγ represents variance of pri-
mary user signal, 2
lσ is the variance of noise. The optimal combination for soft computation of
detection results is to use energy detector for local sensing and combining the results using
weighted Sum[142].
2.2.2 Decision fusion
The decision fusion strategy for cooperative spectrum sensing algorithms is to transmit on-
ly one bit i.e. “1” or “0” to the fusion centre regarding presence or absence of a primary user.
Thus, this method not only achieves the cooperative gains but also helps in optimizing energy
consumption. Some of the combination rules are given by[23, 143]:
2.2.2.1 OR Rule
In this rule, if one of the spectrum sensors sends a “1” as a decision to fusion centre, FC
decides in favour of presence of a PU. Assuming independent local sensors, the global values
of detection and false alarm are given as:
K (i)
d i 1 dP 1 (1 P )
(2.8)
and
30
K (i)
f i 1 fP 1 (1 P )
(2.9)
2.2.3 AND Rule
In this rule, fusion center decides in favor of “1” only if all the sensors send “1” as their
local decisions. The global decisions are computed by:
1 ,
N
d i d iP P
(2.10)
And
1 ,
N
fa i fa iP P
(2.11)
2.2.3.1 L out of N Rule
Under this rule, detector decides in favour of presence of a PU only if L decisions (from to-
tal N) or more are computed as “1”s. The global probabilities are given by:
, ,
0
(1 ) (1 )N L
N L i L i
d d i d i
i
NP P P
L i
(2.12)
And
, ,
0
(1 ) (1 )N L
N L i L i
fa fa i fa i
i
NP P P
L i
(2.13)
On the other hand, each of the local sensors can send multiple bits of decision to the FC so
that the fusion centre could decide more reliably, one such algorithm is presented in [116].
Consider the noisy channel between local sensor and fusion centre, in [144] authors study
cooperative spectrum sensing model that incorporates the imperfect transmission channel. Fur-
31
thermore, several optimal and suboptimal algorithms are analysed including non-cooperative
and cooperative sensing techniques.
In [145] , authors improve the reliability of sensing algorithm for hard combining algo-
rithm by incorporating quality information in addition to one-bit decision. The proposed
scheme requires computing three thresholds at the local sensor. First threshold is determined
by a pre-assigned probability of false alarm (Pfa) while the other two optimal thresholds are
derived using distance criteria.
2.3 Energy Efficiency
Wireless transmissions require higher energy due to multimedia communications. Addi-
tionally, cognitive radios are required to sense the RF spectrum for unused spectrum spaces in
addition to transmissions. Hence, energy efficient and time-saving sensing algorithms are
highly preferred over classic. These algorithms include sequential testing rule, censoring, clus-
tering and sleep-mode operation. Authors in [146] present a two-stage spectrum sensing
algorithm that not only improves reliability of sensing performance but also results in lower
mean sensing time. Similarly, several authors proposed algorithms to improve ROC as well as
reduce detection time [147-151].
2.3.1 Sequential Testing
Sequential Testing or Sequential Probability Ratio Test (SPRT) requires variable number of
samples to compute the decision regarding presence or absence of a PU in comparison to the
fixed number of samples required in classical NP Lemma. The computation of sensing number
for making a confident decision in support of a hypothesis requires two thresholds i.e. and
' [152-159].
In [160], authors apply SPRT on single radio sensor to lower the number of sensing. In
[161], all the sensors send their LLRs to the Fusion Centre where an SPRT is carried out.
32
Thereby, only the results of required sensors are used to achieve the specified values of proba-
bilities. This results in less number of sensing radios which consequently produces energy
efficiency.
2.3.2 Censoring
Censoring is a process to limit active number of sensors participating in a collaborative de-
cision process for spectrum sensing algorithms. By reducing the number of active users
considerable amount of energy can be saved. This results in not only an energy efficient sens-
ing algorithm for the secondary access of spectrum but also reducing the control bandwidth of
the channel due to reduced number of reporting cycles among the sensors and the fusion centre
[23, 115, 142, 162] .
In [143], authors present a simple censoring rule to minimize the number of sensing bits
reported to the FC. Similar to the SPRT, the test statistic ( ) is compared with two thresholds
i.e. 1 And 2 . If is lower than 1 or greater than 2 , the decision is made.
Thus, censoring rules help in reducing the energy consumption on reporting data from sen-
sors to fusion centre. Sophisticated techniques are required that can censor the uninformative
and useless information [15].
2.3.3 Clustering
Clustering refers to grouping of cognitive sensors to improve detection performance as
well as to reduce communication range especially for a multi-hop based transmission link
[142, 143, 163, 164]. Various authors have analysed the cluster-based spectrum sensing cogni-
tive radio [143, 165-167]. In [168], four clustering techniques are considered depending on
location information. These are given below:
2.3.3.1 Random Clustering
This CR grouping technique uses location information of both primary users and secondary
users is unknown.
33
2.3.3.2 Reference based clustering
It depends on CR user positions with respect to a given reference.
2.3.3.3 Statistical Clustering
This group of algorithms exploits statistical information and vicinity of CR users when on-
ly position information of SU is available.
2.3.3.4 Distance based sensing
This set of algorithms is used when the position of both PU and CR is known in advance.
The CR near the PU can participate in sensing and thus optimizing the total energy cost of the
system.
2.4 Cognitive Capabilities in Existing IEEE standards
Cognitive capabilities in wireless communication devices promise to improve service qual-
ity requirements of end users but also help in optimizing wireless resources. IEEE Wireless
Regional Area Network is the first standard that allows the secondary use of TV white spaces
(TVWS) to license-exempt devices (secondary users) in opportunistic fashion. Although, it is
quite difficult to find wideband spectrum sensing strategies for secondary access of RF spec-
trum [16] in existing devices but some of the intelligent signal processing capabilities are
introduced in various IEEE standards for better utilization of wireless resources. These stand-
ards include Wi-Fi (IEEE 802.11), Bluetooth (IEEE 802.15.1), Zigbee (IEEE 802.15.4) and
Wimax (IEEE 802.16). A summary of the features is presented.
2.4.1 IEEE 802.11 k
IEEE 802.11 k is an extension of 802.11, the popular standard for wireless LAN services.
This amendment leads WLAN users with efficient management of radio resources. WLAN
access point (AP) collects various statistical measurements of channel including channel load
34
report, RSSI etc. to re-distribute the users with access points according to dynamic traffic re-
quirements.
2.4.2 Bluetooth
Bluetooth operates in 2.4 GHz band which is an unlicensed band [169-172]. This band is
shared by many users including IEEE 802.11 b/g (WLAN), cordless telephones, microwave
ovens etc. Sharing of unlicensed bands create harmful interference to Bluetooth users. Thus,
an improvement is added in classical standard namely Adaptive Frequency Hopping (AFH) to
improve communication performance under coexisting scenario [16]. AFH uses a sensing
algorithm to determine if there are other users in ISM band and collects channel statistics to
identify unoccupied channels to transmit its data. The Channel metrics that can be exploited
for selecting the RF channel to transmit user transmissions include packet error rate (PER), bit
error rate (BER), received signal strength indicator (RSSI), carrier to interference and noise
ratio (CINR) and other parameters. These statistical parameters can be used to classify the
transmission channel as good, bad or unknown. Thus, cognitive capabilities can improve the
performance of Bluetooth systems significantly [173-175].
2.4.3 IEEE 802.22
IEEE 802.22 standard was developed in response to the efforts of FCC. It is also known as
first cognitive radio standard. This standard allows the devices to use spectrum in secondary
fashion [109, 176-178]. Spectrum sensing requirement is considered to be one of the most
attractive feature of cognitive devices. The sensing is applied in two stages i.e. fast and fine
sensing [109, 177, 179]. Fast sensing involves energy detector and fine sensing is
accomplished by more sophisticated sensors [178, 180-182]. The sensing techniques that are
included into the standard comprises of energy detector, waveform detector or MFD.
Secondary base station will manage the sensing step by distributing the sensing load in
subscriber station thus this is an example of centralized collaborative sensing. Moreover, all
35
the 802.22 BSs will also be equipped with global positioning system (GPS) to discover the
location information of white spaces.
An alternative sensing methodology i.e. external sensing is also proposed in the 802.22
standard to facilitate the secondary usage of low power devices. This architecture recommends
the sensing of Licensed Users through a separate system which announces the results through
high power beacons. These signals are monitored by the low power smart devices to select the
unused spectral spaces [8, 183, 184].
The spectrum sensing algorithms proposed and investigated into this dissertation use ener-
gy detection rule due to their computational efficiency and ease of implementation. The
techniques are simulated under IEEE 802.22 framework.
2.5 Challenges
In this section, a brief summary of challenges is presented along with proposed solutions.
2.5.1 Correlated Multiple Antenna assisted Spectrum Sensing
Multiple antenna radios provide diversity to the receiver. In case of spectrum sensing cog-
nitive radios, multiple antenna elements improve detection probability of the cognitive radio.
Furthermore, due to spatial diversity, the signal to noise ratio (SNR) of the radio is also im-
proved significantly. The cooperative gains are achieved if the antenna elements are
independent and identically distributed (i.i.d.). However, in case of correlated sensing results,
the detection probability deteriorates considerably. Authors [91] compute detection probabil-
ity of cognitive radio using partial test statistic. This method requires larger number of
computations, resulting in in higher computational cost as well as higher computational time
and larger energy consumption during sensing period.
In this research work, a computationally efficient spectrum sensing algorithm is applied to
the above problem that uses linear test statistic to compute the probability of detection for
36
cognitive radio systems. The proposed scenario considers the multiple antenna elements as
correlated. Thus, to improve the detection probability light weight cooperation scheme is also
employed that improves the detection probability of cognitive network. The proposed algo-
rithm is derived and computed using closed-form expressions. Additionally, the proposed
architecture can also be applied to alleviate hidden-node issue, as shown in Figure 1.3, in clas-
sical wireless systems.
2.5.2 Collaborative Spectrum Sensing under Suburban Environments
Collaborative spectrum sensing is proposed in literature to improve detection probability in
cognitive radio systems. The detection probability is formulated and investigated using expo-
nential correlation model, also known as Gudmundson‟s exponential correlation model.
However, experimental studies in both urban and suburban environments suggest that the ex-
ponential correlation model best fits the realistic data of urban environments and suburban
environment can best be modelled through double exponential correlation model. Hence, in
the proposed solution, the problem of collaborative spectrum sensing under suburban envi-
ronments is formulated using double exponential correlation model. Asymptotic probability of
detection under double exponential model is also derived. The performance is compared with
Gudmundson‟s exponential model. Hence it is verified that the proposed model provides im-
proved detection performance in comparison with classical model. This is the first research
work that proposes and incorporates double exponential correlation model in cognitive radio
networks in replacing Gudmundson‟s exponential correlation model.
2.5.3 Mobility driven Cognitive Radios
Unlicensed access to wireless communications for vehicular devices is as important as sta-
tionary and portable devices. Future vehicular communications will require smart devices to
provide ubiquitous connectivity. Thus, algorithms are required to efficiently and reliably sense
the RF spectrum for vehicular cognitive radios. A hard decision combining based spectrum
sensing architecture is proposed to improve detection performance of smart radios under ve-
37
hicular channels. The sensing results are assumed to be correlated under both urban and sub-
urban environments. The performance of the proposed setup is quantified in terms of Region
of Convergence (ROC).
38
3 Multiple Antenna Assisted Spectrum Sensing under Cor-
related Antenna Elements
3.1 Introduction
Multi-antenna based wireless devices are a preferred method of use in current standards as
well as future wireless systems. MIMO systems use multiple antenna elements on transmitter
as well as on receiver side. Multiple antennas based sensing radios produce reliable detection
probability results. The diversity gains are more prominent when the antenna elements are as-
sumed i.i.d. However, the performance of the wireless devices deteriorates significantly as the
correlation among the elements increases. Hence, the correlation among the antenna elements
is directly proportional to the amount of deterioration in detection systems [185]. Multiple
antenna based wireless systems are significantly useful under low SNR regimes. In such cases,
the received signal strength is so weak that the decision block of receiver is unable to distin-
guish between presence and absence of a primary user [131, 133, 186]. Various authors have
addressed the issue of spectrum sensing cognitive radios with multiple antennas [91, 187,
188]. Authors in [91] address the problem of correlated multiple antenna elements for cogni-
tive radio applications, however, the test statistic is formulated using partial test statistic.
The objective of this chapter is to derive detection performance for multiple antenna assist-
ed spectrum sensing cognitive radio under correlated shadowing. Additionally, it is also
required to improve detection performance.
A computationally efficient technique is used in this chapter i.e. linear test statistic to de-
rive the detection problem under correlated multiple antenna elements. Performance of the
detector is improved considerably using light weight cooperation among sensing radios. (One
bit decision combination is also known as light-weight cooperation. This is in comparison to
the soft combination that requires complete LLRs of sensing radio to be submitted to FC). Var-
ious decision combining rules are compared. These include OR, AND, majority and n-ary rule
(using n number of decisions).
39
3.1.1 Contributions
Receiver Operating Characteristic (ROC) is computed for a multiple antenna assisted
cognitive radio using linear-test statistic which is computationally-efficient technique
in comparison to the partial test statistic
Analysed the impact of received signal samples on the relation between false-alarm
probability and received power
Analysed ROC performance under ideal (error-free) and erroneous BSC Channel with
10-3
error probability
3.1.2 Organisation
The remainder of this chapter is organized as follows. Section 3.2 presents the related
work. Section 3.3 presents the System model of the proposed algorithm. Decision computa-
tions on reporting centre are presented in section 3.4. These include both assumptions i.e. ideal
reporting channel and realistic BSC channel with specified eP between sensing nodes and clus-
ter-heads. Simulation results are presented in Section 3.5. Section 3.6 concludes the chapter.
3.2 Related Work
Under low SNR regime, the performance of energy detection based sensors is compro-
mised. Especially, due to the fact that these sensors are unable to distinguish among PU signal,
noise signal and interfering user [2]. To improve detection performance under such environ-
ments, several techniques have been proposed in literature. These techniques include improved
threshold detection algorithms [189], multiple antenna detectors [187, 188, 190], spectral cor-
relation based detectors [126, 191, 192] and improved energy detectors [136, 188]. Studies
suggest that under low SNR regimes, spectral correlation based detectors (also known as sig-
nature detectors) outperform the semi-blind detector schemes such as energy detectors.
Multiple-antenna based sensing algorithms show improved performance under low SNR by
exploiting spatial diversity. However, the diversity gains reduce as the correlation among the
sensing elements increases. Thus, it is verified by the authors in [91] that the performance deg-
40
radation of the energy sensors is directly proportional to the amount of correlation. Authors
[92] study the performance of multiple antenna detectors under equally and exponentially cor-
related and linear–array under fading channels. Additionally, authors in [187] derive multiple
antenna assisted detectors using generalized likelihood ratio test. These detectors are preferred
especially when the channel gains and other statistical information about primary user and
noise power is not available. Additionally, it is also shown that these detectors perform almost
optimally even in the presence variations in noise power. Authors [193] derive the perfor-
mance of multiple antenna aided detectors using soft decision combining algorithms i.e.
maximal ratio combining and selection combining to improve performance under low SNR.
Hence, it is verified that even under moderate SNR, the performance improvement is signifi-
cant. Additionally, it is also shown that the MRC based multi-element detector gives an upper
bound for sensor performance due to the fact that this detector also exploits channel infor-
mation.
Authors [190] propose completely blind algorithms to detect the presence of spectral holes.
These parameters include characteristic function that can be evaluated by Fourier transform of
PDF of received signal samples, polarity coincidence array [194] and dimension estimation
based minimum description length (MDL) [195] . The performance of these detectors does not
depend on the noise variance estimation, hence preferred for spectrum sensing purpose under
low SNR regime.
3.3 System Model
In this section, spectrum sensing model for cognitive radios is presented with correlated
multiple-antenna elements. Local sensors detect the presence of a primary user by using ener-
gy detection algorithm. This algorithm is also a preferred choice in case of spectrum sensing
applications and also proposed in 802.22 standard for fast sensing purpose [178]. Cognitive
users compute the detection probability of licensed activity in a frequency band and inform the
41
FC. After collecting results from several cognitive sensors, FC combines the decisions using
hard decision combining techniques and broadcasts the computed decisions on control bands
to secondary users. SUs upon receiving information about unused RF bands transfer their
transmissions on those unused spectral slots upon getting the information from FC. The sys-
tem model is shown in Figure 3.1.
3.3.1 Computation of Receiver Operating Characteristic (ROC) for Local Sensors
Local sensors use energy detection technique for sensing the licensed activity in a specific
frequency band in the given geographic area. Energy detection is a semi-blind algorithm that
requires only noise floor to set the detection threshold. As these detectors do not require any
aprior information of primary users, hence these are widely used in cognitive radio domain.
The received signal samples are collected and squared to compute test statistic. Inactivity of
primary activity is represented through H0 and presence of the primary users is denoted by H1.
Probabilities of detection and false alarm for the above sensors under AWGN channel are giv-
en by Equation (3.2) and (3.3):
1
2( )
0
lY r n
n
(3.1)
( 2 , )P Qd u
(3.2)
( , )2
( )
u
Pfa u
(3.3)
In above equations, u is the product of bandwidth and time and is the ratio of signal to
noise energy. MQ ( , ) is the Marcum-Q function, (.,.) denotes upper incomplete Gamma
function and (.) represents Gamma function.
42
Figure 3.1 Proposed Architecture for Correlated Energy Detector with Cooperation
3.3.2 Multi-Antenna aided Sensing
Local detectors sense RF spectrum using multiple antennas. These antennas are assumed to
result in correlated sensing. The received signal at a multiple antenna aided sensing node can
be formulated using multivariate Gaussian random distribution as:
0 0
1
( , ) ;
( , ) ;
0
1
σy
σ
H
H
(3.4)
Where 0σ and 1σ 1 K denote the mean of received signal, while 0 and 1
K K
rep-
resent covariance of received signal under H0 and H1 respectively.
Mean and Covariance Matrices of received signal under both hypotheses can be given by
following equations:
43
0 10 1σ = ×1, σ = ×1P P (3.5)
2 2
0 0 0 12, 0 K 1 KI I Φ
P P PPm m m
(3.6)
In above equations, IK denotes a K×K Identity matrix, Φ represents a K×K covariance ma-
trix and 1 is a K x1 vector of all ones. It is assumed that the cognitive radio is enabled with
multiple antenna elements for sensing purpose. These antennas result in correlated sensing
with each antenna collecting k number of samples. Φ can be defined [51] as:
,
* ,
a ba b
aba b
ba
(3.7)
For a, b = 1, 2... K and 0 ≤ ≤ 1. In above equations, is an antenna correlation coeffi-
cient defined [51]:
2 2exp[ 23 ( ) ]
c
l
(3.8)
Ω denotes angular speed, λc represents wavelength of the received signal and l shows the
space between two adjacent elements of multiple antenna. In [196], it is shown that under giv-
en configuration, Φ is a symmetric Toeplitz matrix [197]. These matrices are both Centro
symmetric and bisymmetric.
3.3.3 Hypothesis Testing
In this section, decision statistic regarding presence or absence of primary users in a specif-
ic RF band is derived using Neyman Pearson Lemma. Expressions of probabilities of detection
and false alarm are also derived in closed-form. It is assumed that the average power of re-
ceived signal at a sensing node is less than the noise [198]. Probability density functions under
both null and alternative hypothesis are given by:
44
1 1 1( ) exp{ ( ) ( )}21
2 2(2 )
Tfk
y y -σ y -σ0 0 0 0
0
(3.9)
1 1 1( ) exp{ ( ) ( )}21
2 2(2 )
Tfk
y y -σ y -σ1 1 1 1
1
(3.10)
Likelihood Ratio Test can be formulated as:
( ; )( )
( ; )
1 1 1exp{ ( ) ( )}21
2 2(2 )
1 1 1exp{ ( ) ( )}21
2 2(2 )
H
H
Tk
Tk
y1y
y0
y -σ y -σ1 1 1
1
y -σ y -σ0 0 0
0
(3.11)
Additionally, it is also assumed that covariance matrix under null and alternative hypothe-
sis are almost equal.
0 1
. Taking log on both sides:
1 1 1log ( ) log2
0 T Ty y y y y
0 0 0 1 1 11
(3.12)
; y = y σ y =y σ0 0 1 1
(3.13)
Thus, comparing test statistic with threshold:
1
0
H
H
Tα y
(3.14)
The variables in above equation are defined as:
1( ) ;T T T -1 T -1α σ σ Σ η σ Σ σ σ Σ σB B BA A A
(3.15)
Using above relations, probabilities of detection and false alarm are expressed as:
45
0Pr( )
1FP H H
( )
T
AF
TP Q
(3.16)
1 1Pr( )=DP H H
( ) ( )( )
T T
F A BD
T
PP Q
(3.17)
Detection probability can be exprerssed in terms of false alarm probability:
1( ( ) ) T
D FP Q Q P
(3.18)
3.4 Decision Computation on Fusion Centre
Each set of cognitive sensors detects the presence of licensed activity in selected RF band
and submits a one bit decision to reporting centre also known as fusion centre. Upon collecting
one bit decisions from several sets of sensors, FC jointly combines all the decisions using hard
decision combining rules i.e. AND, OR, m-out-of-n or n-ary. After computing the decision, FC
announces RF band as idle or busy. The final decision is then broadcast via a broadcasting
channel to all the secondary users. After receiving the information from FC, SUs either shift
their transmissions on RF band (if channel is idle) or identify other RF bands for opportunistic
use of RF spectrum (if the channel is busy). The channel between cluster-heads and the report-
ing centre can be considered as ideal (error-free) or realistic BSC with specified Pe. Under
ideal wireless channel condition, between cognitive sensor and cluster-head, decision can be
computed using following equations [130]:
( ) (1 )
D D D
nn i n iPii m
Q P
(3.19)
46
( ) (1 )
FA FA
nn i n iQ P PFA ii m
(3.20)
In above expressions, DP and
FAP denote the probabilities of detection and false alarm con-
cluded by sensors. DQ and
FAQ denote cumulative probabilities of detection and false-alarm
respectively, computed by FC.
Assuming the reporting channel between cluster-heads and the FC as a Binary Symmetric
Channel (BSC) [18]. The channel transmits “0” or “1”. The transmitted bits on the channel are
flipped with a cross-over probabilityeP . The cumulative probabilities of detection and false
alarm with specified probability of error (eP ) can be computed using following expressions
[130].
( ) (1 ), , ,
nn i n iQ p pFA erroneous FA error FA errorii m
(3.21)
( ) (1 ), , ,
nn i n iQ p pD erroneous D error D errorii m
(3.22)
(1 ) (1 ),
p p p p pe eD error D D (3.23)
(1 ) (1 ), fa
p p p p pe eFA error fa (3.24)
D,erroneousQ and FA,erroneousQ represent cumulative values of probabilities for detection and false
alarm respectively with probability of error ( ep ). Probabilities with error at cluster-heads are
denoted with by D,errorp and FA,errorp respectively.
3.5 Simulation Results
In this section, detection performance of the proposed multi-antenna aided spectrum sens-
ing scheme is simulated and analysed. It is assumed that antenna elements produce correlated
47
shadowing results. Furthermore, a hard decision combining based strategy is also evaluated.
The decisions are computed using OR, AND, m-out-of-n and n-ary. The proposed algorithm is
simulated using parameters from IEEE 802.22. Receiver Operating Characteristic (ROC) is a
key tool to distinguish between optimal and suboptimal detectors [199]. ROC curve is ana-
lysed under hard decision combining strategy error-free ideal channel and erroneous BSC
Channel. Numerical values are obtained from IEEE 802.22 standard for Cognitive Radio:
=0P -95.2dBm , =1P -114dBm , W 6MHz , T 1ms , dP = 0.9 , 0
Cλ =0.5 , Cl = λ / 8 , two sensing an-
tenna elements.
-124 -122 -120 -118 -116 -114 -112 -110 -108 -106 -104
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Received Power [dBm]
Pro
ba
bil
ity
of
Fa
lse A
larm
No.of Samples=5
No.of Samples=20
No.of Samples=50
Figure 3.2 False-Alarm Probability relation with Received Power
Figure 3.2 shows the relation between PFA and PR (received signal power) under different
sample sizes. The size is assumed to be 5, 20 and 50 samples. For 0.1 probability of false
alarm, the detector using 50 samples of received signal can detect the power level of -118
dBm, while the detector with 20 and 5 samples cannot. This graph shows that the detector with
larger samples can easily detect a weak signal. Based on the performance, it is recommended
48
to use the detector with larger sample size. However, cognitive radios operate in secondary
fashion and hence, they have to compute decision of primary activity using minimum number
of samples in shortest time. Hence, there is a trade-off between the sensing duration and effi-
cient sensing of weak primary signals for cognitive radios.
Figure 3.3 ROC Performance curves under OR based Hard Decision Combining Rule
Figure 3.3 compares the ROC performance under OR based decision combining strategy
with independent sensor having multiple antenna elements. This architecture involves the use
of a FC, that collects the one bit decisions from sensors i.e. „1‟ for active primary users in
specific RF band and „0‟ for the absence. The decisions are combined using OR rule. After
collecting the results, FC computes probabilities of detection and false alarm and informs sec-
ondary users through broadcast bands regarding available white spaces. The wireless channel
between sensing nodes and FC is assumed ideal i.e. without any error probability. The graph
shows clearly that the cooperation among two radios results in significant performance im-
provement in comparison to independent spectrum sensor. Similarly, cooperation among four
49
users results in much more improvement. The results suggest light-weight cooperation based
spectrum sensing architecture should be a preferred choice in comparison to a single sensor.
10-4
10-3
10-2
10-1
100
10-4
10-3
10-2
10-1
100
Probability of False Alarm
Pro
ba
bil
ity
of
Dete
cti
on
Single User
OR Users =4
Majority Users=5
AND Users=5
n-ary Users=5
Figure 3.4 Compares Hard Decision Combining Rules under BSC channel with
Pe=10-3
Figure 3.4 compares the performance of various hard decision combining rules at the re-
porting centre. It is assumed that the wireless communication channel between sensing nodes
and FC or cluster-head is BSC with Pe=10-3
. The simulation results indicate that the OR based
decision rule outperforms other fusion techniques. And after OR, majority decision combining
(using 5 users) performs better. The results clearly advocate the use of hard decision combin-
ing approach especially OR based fusion in comparison to single sensor.
50
3.6 Conclusion
In this chapter, an investigation of the correlated multiple-antenna aided spectrum sensing
scheme is presented using linear test statistic. The proposed technique requires less number of
computations. Hence, it is recommended to use in future wireless systems using collaborative
sensing. The performance of the proposed spectrum sensing architecture is presented in terms
of error probabilities i.e. missed-detection and false alarm. It is shown that as the number of
samples are increased; the weak primary signals can easily be detected. However, in realistic
scenarios, it is impractical to assume that cognitive radios will be able to exploit larger number
of samples. Additionally, fading makes it more difficult for cognitive sensors to compute deci-
sions using less number of samples. Hence, a cluster-driven architecture is presented to
improve detection performance.
51
4 Collaborative Spectrum Sensing under Suburban Envi-
ronments
4.1 Introduction
Collaboration and cooperation in cognitive radio systems helps in realizing efficient and re-
liable sensing results especially under fading and shadowing. Under shadow-faded channels, it
is difficult for a single user to detect the presence of primary-user activity. Shadow-fading re-
fers to a fading condition where high-rise buildings, trees and other entities disrupt the
communication path between transmitter and receiver. Hence, these techniques aid in produc-
ing reliable sensing results. Additionally, the sensing requirements of a cooperative sensor are
flexible as compared to an independent sensing radio. The cooperation models include central-
ized and decentralized sensing architectures. In centralized schemes, RF detectors submit their
sensing results to a common base station or fusion centre for computation of global probabili-
ties of detection and false alarm. This method is also suggested in first IEEE standard for
WRAN. In decentralized schemes, sensing radios group themselves in an arbitrary fashion for
decision computation. Any sensing member in these schemes may perform as a fusion centre
hence no centralized radio is required to compute global decisions.
Correlated sensing is the result of shadowing in wireless systems. The sensing performance
of the spectrum sensors is highly deteriorated due to correlation. The impact of shadowing is
incorporated by authors [196, 200-202] using classic exponential correlation model i.e. Gud-
mundson‟s exponential correlation model. This model introduces the impact of shadowing
under both urban and suburban environments. However authors observe through experimental
results that the Gudmundson‟s exponential correlation model best fits the data of shadowing in
urban environments. However, under suburban environments the sensing performance better
fits the correlation behaviour of double exponential correlation model [93].
52
The objective of this chapter is to introduce a new correlation model for spectrum sensing
cognitive radios under suburban environments. It is also required to compare the performance
of proposed model to the classic Gudmundson‟s exponential correlation model.
In this chapter, asymptotic probabilities of detection and false alarm are derived for subur-
ban environments using double exponential correlation model. The performance metric of the
proposed sensing algorithm is also compared with classic Gudmundson‟s exponential correla-
tion model.
4.1.1 Contributions
Asymptotic detection performance is derived for collaborative spectrum sensing
radios using double exponential correlation model
Performance of the proposed collaborative spectrum sensing algorithm is compared
with classic Gudmundson’s exponential correlation model under suburban
environment.
4.1.2 Organisation
The rest of the chapter is organized as follows. Literature review is presented in section
4.2.The proposed spectrum sensing model is presented in section 4.3. Asymptotic detection
probability for the proposed model under suburban environments is also derived in this sec-
tion. Section 4.4 presents simulation and discussion of the proposed model. 4.5 concludes the
chapter.
4.2 Related Work
Collaborative spectrum sensing provides a way to improve detection results under fading
environments. In [203], authors optimize collaborative sensing algorithms in terms of opti-
mum decision fusion for soft and hard decision combining techniques. It is argued that the
53
optimal decisions at FC must incorporate SNR values of SUs as well as channel gains. A ge-
netic algorithm is also proposed for soft decision combining techniques. In [204] , authors
present collaborative sensing algorithms to improve reliability of sensing results by exploiting
spatial and temporal correlations of SUs. Hence, authors verify that the proposed algorithms
guarantee better accuracy than the existing ones through numerical simulations. In [205], au-
thors propose the use of onion-peeling approach to provide a defence against suspicious SUs.
Furthermore, suspicious level of sensing nodes is also calculated. Simulation results verify that
the malicious users degrade detection performance significantly. Thus, sensing performance
can be improved using proposed techniques. In [206], authors propose punishment strategy for
attackers so that malicious activities can be evaded. In practical sensing scenarios, it is com-
mon to compute decisions regarding presence of spectral holes using limited available
information. The limited availability of sensing information is due to the fading and other
wireless channel impairments. Hence, the chance of computing wrong decisions becomes
high. To resolve this issue, authors [207] formulate the sensing problem using matrix comple-
tion problem. The investigations show that under noiseless channels with small number of
primary users, required detection probabilities were obtained using only 8% of sensing infor-
mation. And 95.5% detection probability was obtained by using 16.8% of information under
larger number of primary users. In [208], it is shown that under i.i.d. Shadowing the detection
probability can be significantly improved by exploiting user-collaboration. Shadowing or
shadow fading results due to appearing of buildings or other entities in the propagation path of
electromagnetic wave. However, due to correlated shadowing the diversity gains are reduced.
In previous works, the detection probability for cognitive radio applications is computed using
Gudmundson‟s exponential correlation model [209] under both urban and suburban environ-
ments. However, authors presented their observation [93], after extensive real-time channel
measurements, that the exponential correlation model best fits the autocorrelation function
under urban environments but the results under suburban environments better follow double
exponential model. Typically urban refers to the environment with high rise buildings, while
54
rural refers to the area with large number of green vegetation. Similarly, suburban is defined as
environment that is combination of the two[210] .
4.3 Spectrum Sensing Model
A collaborative spectrum sensing scenario is considered under correlated shadowing. It is
considered that a large number of sensing radios detect the presence of primary user transmis-
sions. The primary activity is denoted by a large TV transmitter. The sensing model, as shown
in Figure 4.1, is assumed to operate under IEEE 802.22 framework using energy detection al-
gorithm. The received signal y(n) can be denoted as:
( ) ; 0( )
( ) ( ) ; 1
w n Hy n
x n w n H
(4.1)
Where n = 0,1,2,3......k shows the number of received samples, w(n) represents AWGN
and x(n) represents the samples of primary user signal. Under both hypotheses, the received
signal is distributed as Gaussian. As the number of received signal samples n becomes suffi-
ciently large, the distribution of y can be approximated using central limit theorem as:
20 0 0
( )2
1 1 1
;~
;
( , )
( , )y y
s s Hf
s s H
(4.2)
H0 and H1 represent the absence and presence of PU. Mean of received signal is denoted
by 1
0 1, Ks s and 2
2
10, K Ks s represent covariance matrices under null and alternate
hypotheses respectively, 1 represents the vector of 1s. It is assumed that the received signal
comprises of interference signal under null hypothesis, resulting in common covariance matrix
under both hypotheses i.e. 1 0 s s s .
4401s m m
Λ I
K
(4.3)
55
Figure 4.1 Cognitive radio network, sensing TV Transmitter
In above expression, m = τv , τ denotes sensing time and v denotes bandwidth. Λ de-
scribes double exponential correlation covariance matrix with k x k measurements.
, , 1,2,3,4,.......
,
a ba b k
a b
Λ
(4.4)
In above expression,
is defined by [93]:
(1- )1 2
l ll lhe h eA A
(4.5)
In above equation, 1 2,A Al l denote the short and long correlation distances, h shows the
strength between short and long correlation distance and l represents the distance travelled by
the cognitive sensor. Consider the case of secondary users distributed in one-dimension within
a fixed distance L,( 1)
Ll
k, where k represents the number of sensing. Using Neyman Pearson
Lemma, detection probability can be derived as:
2 21 11 0{ 1 1 ( )}
T
d fa
s sP Q Q P
s
(4.6)
56
In above expression, Pd represents probability of detection and P fa represents probability of
false alarm, is a symmetric Toeplitz matrix, also known as Kac-Murdock-Szego matrix
[211] .
(1 ) 211 11
kT
(4.7)
For very large number of sensing,
(1 ) 2lim { }
1k
k
(4.8)
1 21 1{1 (1 ) } 2
lim2
a L a L
k khe h e kk
(4.9)
Assume 1
Lk
x
, , 0k x
1 2lim {1 (1 ) }{1 }0
a x a x Lhe h e
x x
(4.10)
( ) 21 2 2
2
L ha a ha
(4.11)
Where 1
1
1
A
al
and2
2
1
A
al
.
4.4 Simulation Results and Discussion
In this section, collaborative sensing performance, incorporating proposed double exponen-
tial correlation model is investigated and analysed under suburban environments. The
performance of the proposed model is also compared with the classic Gudmundson‟s exponen-
tial correlation model. It is assumed that a very large number of cognitive sensors, placed
equidistantly, sense TV broadcast stations. The numerical computations are performed using
57
parameters from [93, 200], given by:
2
0
2
1 5.19 s s ,fa A1 A22.3, L 100, P 0.001, P 0.9, 25 m, 200 m, h 0.2,
d l l , num-
ber of sensing = 20.
Figure 4.2 compares the performance of the proposed double correlation model with classic
exponential correlation model.
101
102
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Number of Users, k
Pro
ba
bil
ity
of
Mis
sed
Op
po
rtu
nit
y
Double Exponential Model
Gudmundson Exponential Model
Figure 4.2 Relation between Missed Opportunity and No. of Secondary Users
The results in Figure 4.2 show the use of proposed double correlation model for cognitive
radio applications under suburban environments. For the proposed model, missed-detection
probability reaches to almost zero using less than ten secondary users, while the cognitive sen-
sor using Gudmundson‟s exponential model is unable to achieve zero missed-detection even
more than hundred secondary users. The Gudmundson‟s model shows the missed-detection
probability of 0.7 for zero to hundred secondary users.
.
58
4.5 Conclusion
Asymptotic analysis of collaborative sensing algorithm is derived, investigated and ana-
lysed using double exponential correlation model for suburban environments. The results of
proposed model are also compared with the Gudmundson‟s exponential correlation model.
Simulation results verify that the proposed channel model performs significantly better than
exponential model. Due to better detection capability of the proposed sensing model, the
double exponential model should preferably be applied under suburban environments.
59
5 Cooperative Spectrum Sensing under Mobility Driven
Cognitive Radios
5.1 Introduction
Cognitive radios find highly useful applications under vehicular communication domain.
These radios can be used to provide ubiquitous connectivity to the mobile user by exploiting
the RF spectrum in opportunistic fashion. Additionally, it can enable the vehicles to improve
safety of the travellers autonomously through collision avoidance. Furthermore, in-transit pa-
tients, passing through opportunity-window can be provided telemedicine facilities to save
their precious lives. Safe transportation requires different communication modes such as Vehi-
cle to Vehicle (V-V), Vehicle to Infrastructure (V- I) and Infrastructure to Vehicle (I-V) to
establish an adhoc network spontaneously. Vehicle to vehicle connection refers to the commu-
nication link between two vehicles, V-I refer to the connection between vehicle and
infrastructure and I-V refers to the connection between infrastructures to vehicle. These con-
nections are established to gather data on road that enables the driver to travel on specific
roads so that accidents can be avoided. This may result in saving precious lives as well as
property worth millions of dollars.
US transportation department (US DOT) has already started experimentation in 2014 to
enable vehicle to vehicle communication for exchanging basic safety data i.e. speed and posi-
tion ten times per second to prevent imminent collisions. These strategies will enable drivers
to divert their vehicles on appropriate roads to avoid crashes, injuries and incidental deaths.
This will help in protecting the precious lives of travellers as well as improve transportation
resulting in saving of billions of dollars each year.
Tele-healthcare facilities can be provided to critically-ill in-transit patients by measuring
their bio-signals, conducting suitable pathological tests and communicating results with a doc-
tor or health care professionals at a distant hospital [212, 213]. This can be extremely useful
for patients with lethal diseases such as cardiac disease, which is the highest cause of mortality
60
and morbidity in USA [214]. The ailing patients with cardiac disease can be stabilized by
providing appropriate pre-hospital medical facilities under the guidance of health profession-
als. The communication between the patient and healthcare staff can be established through
cognitive radio. These radios can provide seamless connectivity mostly to secondary users so
that essential sensitive data can be communicated reliably. In this chapter, a hard decision
combining based spectrum sensing architecture is presented for vehicular cognitive radio ap-
plications. The proposed model is shown in Figure 5.1
Figure 5.1 Proposed Architecture for Mobility-Driven Cognitive Radio
The objective of this chapter is to propose a three-tier telemedicine system to improve per-
formance on first tier and provide ubiquitous connectivity by using cognitive radio.
The proposed model incorporates the detection results presented by [196]. The decisions
are computed using hard decision combining strategies including OR, AND and majority. The
communication link between primary transmitter and the secondary sensor is considered to
61
produce shadowed results. It is assumed that the channel follows exponential correlation mod-
el. The proposed algorithm is tested under urban and suburban environments. The rest of the
chapter is organised as follows.
5.1.1 Contributions
Compared the performance of a vehicular radio under suburban environments using
double exponential correlation model and Gudmundson‟s exponential correlation
model
Proposed and analysed hard decision combining algorithm for mobility driven cogni-
tive radios under urban and suburban environments
5.1.2 Organisation
Section 5.2 discusses the related work. Section 5.3 presents the proposed technique for
spectrum sensing. Section 5.4 presents the simulation results of the proposed algorithm and
discussion on results and Section 5.5 concludes the chapter.
5.2 Related Work
A brief summary of mobility-aided spectrum sensing algorithms is reviewed in this section.
In [215] , authors analytically derive two parameters i.e. detection capability and sensing ca-
pacity for PU-mobility. Authors conclude that the detection capability is affected by primary
user protection range, network size, mobility model of PU, spatial distribution of cognitive
radio and number of primary users using the same spectrum band. Additionally, it is shown
that the sensing capacity can significantly increase in the presence of PU mobility if PU pro-
tection range is smaller than network size. In [216], authors present challenges and
opportunities for mobility-aware design of cognitive radio networks. It is recommended that
mobility related issues should be addressed from a network point of view.
62
In [217], authors investigate the impact of sensor mobility on the performance of joint op-
timization framework for sensor cooperation and sensing scheduling. It is shown that the
sensing performance improves due to spatio-temporal diversity induced by mobile sensors.
Furthermore, a sensing strategy is also proposed to compute optimal number of cooperative
nodes as well as number of sensing times.
In [196], authors analyze the energy detection based spectrum sensing technique in the
presence of mobile user. It is shown that the mobility of secondary user improves detection
performance by exploiting spatial diversity. Furthermore, a local spectrum sensing architecture
is also proposed with optimal fusion based rule based on likelihood ratios. In this paper, au-
thors use Gudmundson's exponential correlation model to model the sensing results under
urban and suburban environments. Our work is based on this paper. We propose light weight
cooperation among the decisions of sensors. The decision is computed using OR, AND and n-
ary based hard decision combining rule. It is shown that the proposed technique can signifi-
cantly improve the detection performance of mobility-aided spectrum sensing radios.
5.3 Proposed System Model
We consider energy detection based technique for detection of spectral holes in broadcast
bands for opportunistic utilisation of unused spectrum. In this scenario, it is assumed that the
cognitive radio is attached to a vehicle, moving in urban and suburban environments. The re-
ceived signal Y follows following statistics:
1
Y
2
nn 0
2
s ns n
P(P , ) ;H
m
(P +P )(P +P , ) ;H
m
(5.1)
63
Where SP is the variance of received signal under the assumption of the presence of primary
user, nP is the noise variance, m TW is the time-bandwidth product, 1 shows a column vec-
tor of 1s.
0 1
, ( ) s
P 1 P +P 1n n (5.2)
0
2 2 2, N NI I Λn n s nP P PP
m m m (5.3)
In the above expression, µ0 and µ denote mean values and 0Σ , 1Σ are the covariance ma-
trices of received signal under null and alternate hypothesis respectively.
IN is a N×N Identity matrix and Λ s a N×N covariance matrix which is defined as
12
p q
, , 1,2,3,.....p q n and denotes the correlation between two sensing nodes,
while v is the vehicular speed of the mobile sensor. Similarly the received signal under double
exponential correlation model can be described by:
0
2 2 2, N NI I ξn n s nP P PP
m m m (5.4)
Where / /
1 2(1 )
d d d d
A Aue u e , dA1 and dA2 represent short and long correlation
distances, and d represents the distance travelled by cognitive user as in [10]
Assumption 1 0 converts the quadratic expression to linear. Hence, the detection
probability in closed form can be expressed as under:
1-1
d f r
TP =Q(Q (P )- P 1 1 ) (5.5)
64
Pd represents the detection probability, Pf is the false alarm probability, P r is the received
power and is the received SNR.
5.4 Numerical Results & Discussion
In this section, we evaluate performance of the mobility driven spectrum sensing algorithm
under urban and suburban environments. The simulation parameters are taken from WRAN
standard: Received Power is -114 dBm, noise power is -95.2 dBm, sensing time is 1 msec,
bandwidth is equal to 6MHz, and environment constant for urban environments is assumed
0.12 and 0.002 for suburban environments. It is assumed that the probability of false alarm is
0.001. Figure 5.2 compares the spectrum sensing ROC under suburban environment using
double exponential correlation model and classical Gudmundson‟s exponential correlation
model. The double exponential model outperforms the classic model.
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Probability of False Alarm
Pro
ba
bil
ity
of D
etectio
n
Double Exponential
Gudmundson Correlation
Figure 5.2 ROC Comparison under Suburban Environment
65
10-2
10-1
100
10-2
10-1
100
Probability of False Alarm
Pro
bab
ilit
y of
Mis
sed
-Det
ecti
on
Single User
Cooperation OR
AND
Figure 5.3 Cooperation under Urban Environments
Figure 5.3 shows the impact of hard decision combining strategy for urban environments.
The simulation scenario uses Gudmundson‟s exponential correlation model to introduce the
impact of correlation. The missed detection probability values are lower for three users as
compared to a single user case. The results clearly indicate for a cooperation protocol in com-
parison to independent sensing. The decision combination using AND and OR based rules
outperform the single user. After attending the false alarm probability value of 0.1, the ROCs
exchange their performance among themselves. Similarly, Figure 5.4 shows the impact of co-
operation under suburban environments. This simulation uses Gudmundson‟s exponential
correlation model to induce the correlation impact. The results are similar to the urban scenar-
io i.e. the performance of cooperating radio is superior to single radio. Moreover, majority
based decision rule outperforms the other decisions till almost 0.1 PFA
66
10-2
10-1
100
10-2
10-1
100
Probability of False Alarm
Pro
bab
ilit
y of
Mis
sed
-Det
ecti
on
Single User
Cooperation OR
Majority
Figure 5.4 Cooperation under Suburban Environments
5.5 Conclusion
A hard decision combining based strategy is presented for vehicular spectrum sensing ap-
plications. It is verified that the proposed technique significantly improves detection
performance by using light weight cooperation. Two environments are considered i.e. urban
and suburban. Under suburban environments the two channel models are tested i.e. double
exponential correlation and Gudmundson‟s exponential correlation model. The decisions are
computed using OR, Majority and AND combining rules. The proposed algorithm is tested
under both urban and suburban environments.
67
6 Conclusion & Future Issues
6.1 Conclusion
In this dissertation, we derived, investigated and analysed RF spectrum sensing techniques
for cognitive radio applications. The proposed algorithms can be exploited by the current wire-
less standards with cognitive capabilities such as Wimax and WLAN as well as future wireless
systems. Cognitive radio devices are also known as license-exempt or opportunistic devices.
These devices can operate under both licensed as well as unlicensed bands. Thus, it is imprac-
tical for these devices to know the complete information about the primary users, interference
and noise in licensed bands and all the active users in unlicensed bands. So, optimal or coher-
ent detectors are not a preferred choice for spectrum sensing purpose. We use semi-blind
spectrum sensing algorithm i.e. energy detectors. These detectors only require noise variance
for setting detector threshold of the spectrum sensors. This class of algorithms is extensively
used and also recommended for fast sensing phase in the WRAN standard.
This dissertation, can broadly be categorized in three parts. Part one explores the detection
of spectral holes under the condition that the multiple-antenna detector results in correlated
shadowing results. Furthermore, the correlated sensing performance of the detector is im-
proved through light-weight cooperation among sensing radios. The wireless reporting channel
between sensing node and FC is considered both as error free and erroneous. Part two pro-
poses the use of double exponential correlation model for the collaborative sensing radios
under suburban environments. This is the first work that introduces the double correlation
model in spectrum sensing domain. The performance of the algorithm is also compared with
classic exponential correlation model i.e. Gudmundson‟s exponential correlation model. In the
third part light-weight cooperation is incorporated among vehicular cognitive radios to provide
improved detection performance to mobile users. Hence, it is concluded that the performance
68
of the proposed detector can be improved significantly by introducing cooperation among ve-
hicular radios.
Multiple antenna elements provide spatial diversity to wireless communication devices.
Multi-input multi-output technologies are extensively used in wireless standards such as
WLAN, Wimax and LTE, providing higher data rates to the end user. The multiple antenna
aided receiver is used for sensing white spaces. The gains are maximum when the antenna el-
ements produce i.i.d. sensing results. However, as the correlation among sensing elements
increases, the performance of the receiver deteriorates in direct proportion. We derived the
performance of a multiple-antenna aided cognitive radio device using linear test statistic with
correlated sensing results. The performance of the proposed sensing algorithm is investigated
in terms of complimentary ROC. Wireless channel between sensing node and primary user is
assumed Rayleigh faded. To improve the detection performance under fading channel, a light-
weight cooperative strategy is employed that fuses one bit decisions of the cluster-heads on FC
to compute global probabilities of detection and false alarm. After computing these probabili-
ties, FC announces the computed results through control channel to all secondary radios in that
region, so that the devices could exploit the spectrum in secondary fashion. The reporting
channel is assumed ideal i.e. ideal or error free and BSC with 10-3
cross-over probability. The
decisions at the FC are computed using different rules including OR, AND, m-out-of-n and n-
ary. The results are also compared with the sensing results of a single user. Hence, it is verified
that the OR based decision rule outperforms other decision rules. The proposed algorithm is
tested under IEEE WRAN standard.
Collaboration among cognitive radios provides spatio-temporal diversity which is essential
for efficient spectrum sensing under fading channels. The previous research in cognitive radio
domain uses Gudmundson‟s exponential correlation model for modelling shadowing under
urban and suburban environments. This includes both static receiver as well as mobile receiv-
er. However, Albert Algans et al. [93] observed through empirical assessment that the
Gudmundson‟s model exhibits the correlation properties similar to urban cities but double ex-
69
ponential correlation model shows better approximation for suburban environments. Asymp-
totic probability of detection using double exponential correlation model is derived. The
performance of the detector is also compared with Gudmundson‟s exponential correlation
model. Hence it is shown that the proposed model attains minimum Pmd as compared to Gud-
mundson‟s model. To further improve the detection performance, a cluster driven architecture
is proposed that combines the sensing results of cluster-heads. Light-weight cooperation
among clustered-cognitive radios is considered. The wireless channel between sensing radios
and FC is assumed error-free. The decisions are computed suing OR based rule. It is shown
that the cooperation among two and three groups of cognitive radios significantly improves
detection performance of the sensing architecture.
Seamless connectivity is required to vehicular users for travelling safety as well as for
providing communication facilities. Cognitive radio is a novel technology to fulfil these re-
quirements ubiquitously. The proposed spectrum sensing algorithm exploits cooperation
among vehicular radios to provide better detection probability using hard decision combining
approach. The proposed architecture can be employed by the vehicular radios under urban and
suburban environments. The performance metric is simulated using Gudmundson‟s model for
shadow fading results.
6.2 Future Issues
In this section, a brief summary of challenges is presented into the spectrum sensing do-
main for cognitive radio applications.
6.2.1 Correlated Narrowband Noise
Narrowband filters are used in receiving circuitry to filter out non-useful signals and noise
components from desired bands of frequencies. The noise is considered as i.i.d. before apply-
ing filter [97]. However, under realistic wireless channels, noise is correlated. In such
conditions, the received signal should be pre-whitened for using i.i.d. assumption. This can be
70
achieved by using eigenvalue and covariance based detection algorithms. Furthermore it is
seen that the detection performance deteriorates significantly due to correlated sensing antenna
elements.
6.2.2 Interference Intrusion
In spectrum sensing domain, it is assumed that the noise is white and the hypothesis is sim-
ple. White noise refers to the signal with constant power spectral density, whereas noise of
different colours usually refer to different power spectral densities. However, in practical sit-
uations this may not be a valid assumption due to the intrusion of interfering signal with PU
signal. The reason behind this irregularity may be external signals or spurious signals generat-
ed by the ADC non-linearities [97]. Hence, it is difficult for a blind detector i.e. energy sensor
to estimate the dynamic interference power with its dynamic distribution. Neglecting this is-
sue, results in performance deterioration of detectors. In such cases, the detection can be
improved at the device level or through the signal processing techniques. Additionally, signa-
ture detectors can be used in place of blind detectors under low SNR regime for reliable
detection results.
6.2.3 Wideband Sensing
Wideband spectrum sensing techniques focus on detecting unused frequency bands which
exceed the coherence bandwidth of the channel [218-220]. For the receiver circuits, the ADC
must operate at a sampling frequency that should be twice the maximum available frequency
of the signal. Thus, wideband especially that is continuous in GHz is a difficult step to decide
regarding detection. These bands require sophisticated signal processing algorithms. Addi-
tionally, fading in wireless channel further deteriorates the detection performance of the
sensors [221, 222], hence, cooperation is suggested to improve detection performance[223,
224]. Compressive sensing is also applied to exploit the sparcity in the signal. However, due
to time varying behaviour of fading characteristics, sparsity level also varies. For such condi-
tions, adaptive wideband sensing algorithms are used to improve detection efficiency [218,
71
225, 226] . However, more sophisticated techniques are required to improve the detection per-
formance [97, 218, 227].
6.2.4 Complexity
Complexity is one of the most challenging issues to address in spectrum sensing domain.
Computationally-Simple and energy-efficient algorithms are preferred over complex, however,
in certain situations it becomes needful to use complex and higher energy consuming algo-
rithms than the simple ones. Some of those scenarios are given as under:
Detection under shadow fading channels is quite difficult task. This happens due to the
weak received signals, which makes it difficult to distinguish between noise and useful signal.
In such scenarios, cooperative detection is recommended in literature to apply for improving
the performance. Similarly under low SNR regime, Cyclostationary feature based detectors are
a preferred method of sensing in comparison to simple energy detectors due to the fact that
energy detectors cannot distinguish among useful signals, noise and interference signals while
feature detectors can distinguish those. Additionally, further information such as waveform
type, modulation schemes are also required to compute reliable decisions [97]. Thus, signal
identification is complex task.
6.2.5 Quickest Detection
It is a research topic that needs to be explored further. Most of the available techniques for
spectrum sensing consider static channel conditions and simple binary hypothesis testing i.e.
the presence or absence of a primary user signal. However, in practical situations signal esti-
mation is an important step towards realization of a successful detection technique.
Furthermore, wireless channel is a dynamic entity. In such conditions, algorithms such as
GLRT based detection rule, Bayesian learning and marginalization are essential to decide the
presence or absence of a primary user signal [142].
72
6.2.6 Hidden-Node in Wireless Communications
In classical communications, a wireless link cannot be reliably established if the path be-
tween transmitter and receiver faces deep-fading and shadowing. Deep fading refers to the
larger attenuation due to which signal detection becomes difficult task. Similarly, shadowing
refers to the intervention of a building, tree etc. between the communication path between
transmitter and receiver.
Similarly, if the communication path between primary transmitter and cognitive sensor fac-
es deep fading, then it is not possible for a CR to efficiently detect the presence of primary
transmitter. Thus, deciding in favour of the absence of a PU, cognitive user starts its transmis-
sions which create harmful interference for the primary user.
In such conditions, cooperative spectrum sensing is recommended for efficient and reliable
detection of primary users and spectrum holes. Furthermore, energy-efficient cooperative algo-
rithms are required to address the open issues[16].
73
References
[1] A. Goldsmith, Wireless communications: Cambridge university press, 2005.
[2] D. Cabric, S. M. Mishra, and R. W. Brodersen, "Implementation issues in spectrum sensing for cognitive radios," in Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on, 2004, pp. 772-776.
[3] F. C. Commission, "Spectrum policy task force report, FCC 02-155," ed: Nov, 2002.
[4] M. McHenry, "Report on spectrum occupancy measurements," shared spectrum company, 2005.
[5] M. A. McHenry, "NSF spectrum occupancy measurements project summary," Shared spectrum company report, 2005.
[6] M. Mehdawi, N. Riley, K. Paulson, A. Fanan, and M. Ammar, "Spectrum occupancy survey in HULL-UK for cognitive radio applications: measurement & analysis," International Journal of Scientific & Technology Research, vol. 2, 2013.
[7] R. I. Chiang, G. B. Rowe, and K. W. Sowerby, "A quantitative analysis of spectral occupancy measurements for cognitive radio," in Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th, 2007, pp. 3016-3020.
[8] A. Tajer and X. Wang, "Beacon-assisted spectrum access with cooperative cognitive transmitter and receiver," Mobile Computing, IEEE Transactions on, vol. 9, pp. 112-126, 2010.
[9] T. Yucek and H. Arslan, "A survey of spectrum sensing algorithms for cognitive radio applications," Communications Surveys & Tutorials, IEEE, vol. 11, pp. 116-130, 2009.
[10] A. Z. Shaikh and T. Altaf, "Collaborative Spectrum Sensing under Suburban Environments," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 4, 2013.
[11] A. Z. Shaikh and T. Altaf, "Performance Analysis of Correlated Multiple Antenna Spectrum Sensing Cognitive Radio," International Journal of Computer Applications, vol. 50, 2012.
[12] J. Mitola, "Cognitive Radio---An Integrated Agent Architecture for Software Defined Radio," 2000.
[13] S. Haykin, "Cognitive radio: brain-empowered wireless communications," Selected Areas in Communications, IEEE Journal on, vol. 23, pp. 201-220, 2005.
[14] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, "NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey," Computer Networks, vol. 50, pp. 2127-2159, 2006.
[15] I. F. Akyildiz, B. F. Lo, and R. Balakrishnan, "Cooperative spectrum sensing in cognitive radio networks: A survey," Physical Communication, vol. 4, pp. 40-62, 2011.
74
[16] H. Arslan, Cognitive radio, software defined radio, and adaptive wireless systems vol. 10: Springer, 2007.
[17] B. A. Fette, Cognitive radio technology: Academic Press, 2009.
[18] J. G. Proakis, "Digital communications. 1995," McGraw-Hill, New York .
[19] H. Arslan and T. Yücek, "Spectrum sensing for cognitive radio applications," in Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems , ed: Springer, 2007, pp. 263-289.
[20] G. Ganesan and Y. Li, "Cooperative spectrum sensing in cognitive radio, part I: Two user networks," Wireless Communications, IEEE Transactions on, vol. 6, pp. 2204-2213, 2007.
[21] G. Kramer, M. Gastpar, and P. Gupta, "Cooperative strategies and capacity theorems for relay networks," Information Theory, IEEE Transactions on, vol. 51, pp. 3037-3063, 2005.
[22] T. Cover and A. E. Gamal, "Capacity theorems for the relay channel," Information Theory, IEEE Transactions on, vol. 25, pp. 572-584, 1979.
[23] K. Letaief and W. Zhang, "Cooperative communications for cognitive radio networks," Proceedings of the IEEE, vol. 97, pp. 878-893, 2009.
[24] J. N. Laneman, D. N. Tse, and G. W. Wornell, "Cooperative diversity in wireless networks: Efficient protocols and outage behavior," Information Theory, IEEE Transactions on, vol. 50, pp. 3062-3080, 2004.
[25] B. Wild and K. Ramchandran, "Detecting primary receivers for cognitive radio applications," in New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on , 2005, pp. 124-130.
[26] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, "A survey on spectrum management in cognitive radio networks," Communications Magazine, IEEE, vol. 46, pp. 40-48, 2008.
[27] S. M. Dudley, W. C. Headley, M. Lichtman, E. Y. Imana, X. Ma, M. Abdelbar, A. Padaki, A. Ullah, M. M. Sohul, and T. Yang, "Practical Issues for Spectrum Management With Cognitive Radios," Proceedings of the IEEE, vol. 102, pp. 242-264, 2014.
[28] L. F. Minervini, "Spectrum management reform: Rethinking practices," Telecommunications Policy, vol. 38, pp. 136-146, 2014.
[29] I. F. Akyildiz, W.-Y. Lee, and K. R. Chowdhury, "Spectrum management in cognitive radio ad hoc networks," Network, IEEE, vol. 23, pp. 6-12, 2009.
[30] F. Khozeimeh and S. Haykin, "Dynamic spectrum management for cognitive radio: an overview," Wireless Communications and Mobile Computing, vol. 9, pp. 1447-1459, 2009.
[31] Q. Zhao and B. M. Sadler, "A survey of dynamic spectrum access," Signal Processing Magazine, IEEE, vol. 24, pp. 79-89, 2007.
75
[32] G. Gur, S. Bayhan, and F. Alagoz, "Cognitive femtocell networks: an overlay architecture for localized dynamic spectrum access [dynamic spectrum management]," Wireless Communications, IEEE, vol. 17, pp. 62-70, 2010.
[33] J. M. Peha, "Approaches to spectrum sharing," Communications Magazine, IEEE, vol. 43, pp. 10-12, 2005.
[34] R. Etkin, A. Parekh, and D. Tse, "Spectrum sharing for unlicensed bands," Selected Areas in Communications, IEEE Journal on, vol. 25, pp. 517-528, 2007.
[35] D. Čabrić, S. M. Mishra, D. Willkomm, R. Brodersen, and A. Wolisz, "A cognitive radio approach for usage of virtual unlicensed spectrum," in Proc. of 14th IST mobile wireless communications summit, 2005, pp. 1-4.
[36] L. Ma, X. Han, and C.-C. Shen, "Dynamic open spectrum sharing MAC protocol for wireless ad hoc networks," in New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on , 2005, pp. 203-213.
[37] I. Christian, S. Moh, I. Chung, and J. Lee, "Spectrum mobility in cognitive radio networks," Communications Magazine, IEEE, vol. 50, pp. 114-121, 2012.
[38] H.-j. Liu, Z.-x. Wang, S.-f. Li, and M. Yi, "Study on the performance of spectrum mobility in cognitive wireless network," in Communication Systems, 2008. ICCS 2008. 11th IEEE Singapore International Conference on , 2008, pp. 1010-1014.
[39] W.-Y. Lee and I. F. Akyildiz, "Spectrum-aware mobility management in cognitive radio cellular networks," Mobile Computing, IEEE Transactions on, vol. 11, pp. 529-542, 2012.
[40] S. Nejatian, J. Abolarinwa, S. Syed-Yusof, N. A. Latiff, and V. Asadpour, "Characterization of spectrum mobility and channel availability in CRMANETs," in Proc. of the International Conference on Advances in Mobile Networks and Communication-MNC, 2012, pp. 12-16.
[41] www.pta.com.pk
[42] M. I. IMRAN, "CELLULAR MOBILE SUBSCRIBERS TREND IN PAKISTAN," Asian Journal of Multidisciplinary Studies, vol. 3, 2015.
[43] R. Subramanian, "The (Continuing) Evolution of India's Telecom Policy," Communications of the IIMA, vol. 8, p. 33, 2008.
[44] S. Y. Imtiaz, M. A. Khan, and M. Shakir, "Telecom sector of Pakistan: Potential, challenges and business opportunities," Telematics and Informatics, vol. 32, pp. 254-258, 2015.
[45] F. Pujol, "“Mobile traffic forecasts 2010-2020 & offloading solutions," IDATE Consulting and Research, May, vol. 15, 2011.
[46] J. Mitola, "Cognitive radio for flexible mobile multimedia communications," in Mobile Multimedia Communications, 1999.(MoMuC'99) 1999 IEEE International Workshop on, 1999, pp. 3-10.
76
[47] H. Arslan and S. Ahmed, "Applications of cognitive radio," in Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems, ed: Springer, 2007, pp. 383-420.
[48] J. Wang, M. Ghosh, and K. Challapali, "Emerging cognitive radio applications: A survey," Communications Magazine, IEEE, vol. 49, pp. 74-81, 2011.
[49] D. Maldonado, B. Le, A. Hugine, T. W. Rondeau, and C. W. Bostian, "Cognitive radio applications to dynamic spectrum allocation: a discussion and an illustrative example," in New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on, 2005, pp. 597-600.
[50] S. Ball, A. Ferguson, and T. W. Rondeau, "Consumer applications of cognitive radio defined networks," in First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN’05), 2005, pp. 518-525.
[51] P. Phunchongharn, E. Hossain, D. Niyato, and S. Camorlinga, "A cognitive radio system for e-health applications in a hospital environment," Wireless Communications, IEEE, vol. 17, pp. 20-28, 2010.
[52] F. Granelli and H. Zhang, "Cognitive ultra wide band radio: a research vision and its open challenges," in 2nd International Workshop on Networking with Ultra Wide band and Workshop on Ultra Wide Band for Sensor Networks , 2005, pp. 4-6.
[53] F. C. Commission, "Connecting America: The national broadband plan," ed, 2010.
[54] F. C. Commission, "Mobile broadband: the benefits of additional spectrum," Omnibus Broadband Initiative, p. 2, 2010.
[55] J. Sydor, "Coral: A wifi based cognitive radio development platform," in Wireless Communication Systems (ISWCS), 2010 7th International Symposium on , 2010, pp. 1022-1025.
[56] M. Sherman, A. N. Mody, R. Martinez, C. Rodriguez, and R. Reddy, "IEEE standards supporting cognitive radio and networks, dynamic spectrum access, and coexistence," Communications Magazine, IEEE, vol. 46, pp. 72-79, 2008.
[57] M. Nekovee, "A survey of cognitive radio access to TV white spaces," in Ultra Modern Telecommunications & Workshops, 2009. ICUMT'09. International Conference on, 2009, pp. 1-8.
[58] M. Nekovee, "Cognitive radio access to TV white spaces: Spectrum opportunities, commercial applications and remaining technology challenges," in New Frontiers in Dynamic Spectrum, 2010 IEEE Symposium on , 2010, pp. 1-10.
[59] A. M. Wyglinski, M. Nekovee, and T. Hou, Cognitive radio communications and networks: principles and practice: Academic Press, 2009.
[60] D. Jiang, V. Taliwal, A. Meier, W. Holfelder, and R. Herrtwich, "Design of 5.9 GHz DSRC-based vehicular safety communication," Wireless Communications, IEEE, vol. 13, pp. 36-43, 2006.
[61] H. Hartenstein and K. Laberteaux, VANET: vehicular applications and inter-networking technologies vol. 1: Wiley Online Library, 2010.
77
[62] W. C. Collier and R. J. Weiland, "Smart cars, smart highways," Spectrum, IEEE, vol. 31, pp. 27-33, 1994.
[63] K. D. Singh, P. Rawat, and J.-M. Bonnin, "Cognitive radio for vehicular ad hoc networks (CR-VANETs): approaches and challenges," EURASIP Journal on Wireless Communications and Networking, vol. 2014, pp. 1-22, 2014.
[64] Y. Sun and K. R. Chowdhury, "Enabling emergency communication through a cognitive radio vehicular network," Communications Magazine, IEEE, vol. 52, pp. 68-75, 2014.
[65] M. Jalil Piran, Y. Cho, J. Yun, A. Ali, and D. Y. Suh, "Cognitive Radio-Based Vehicular Ad Hoc and Sensor Networks," International Journal of Distributed Sensor Networks, vol. 2014, 2014.
[66] K. Tsukamoto, Y. Oie, H. Kremo, O. Altintas, H. Tanaka, and T. Fujii, "Implementation and Performance Evaluation of Distributed Autonomous Multi-Hop Vehicle-to-Vehicle Communications over TV White Space," Mobile networks and applications, pp. 1-17, 2015.
[67] C. Pattichis, E. Kyriacou, S. Voskarides, M. Pattichis, R. Istepanian, and C. N. Schizas, "Wireless telemedicine systems: an overview," Antennas and Propagation Magazine, IEEE, vol. 44, pp. 143-153, 2002.
[68] V. Garshnek and F. M. Burkle, "Applications of telemedicine and telecommunications to disaster medicine historical and future perspectives," Journal of the American Medical Informatics Association, vol. 6, pp. 26-37, 1999.
[69] H. Cao, V. Leung, C. Chow, and H. Chan, "Enabling technologies for wireless body area networks: A survey and outlook," Communications Magazine, IEEE, vol. 47, pp. 84-93, 2009.
[70] M. Chen, S. Gonzalez, A. Vasilakos, H. Cao, and V. C. Leung, "Body area networks: A survey," Mobile networks and applications, vol. 16, pp. 171-193, 2011.
[71] S. Mehfuz, S. Urooj, and S. Sinha, "Wireless Body Area Networks: A Review with Intelligent Sensor Network-Based Emerging Technology," in Information Systems Design and Intelligent Applications, ed: Springer, 2015, pp. 813-821.
[72] S. Herekar, "Ambulatory Remote Monitoring and Emergency Alert System For Patients Using Various Body Sensors," Venus, vol. 1, 2015.
[73] G. Searle, D. Burns, B. Burns, D. Mason, and C. Hwang, "Wireless Communication for On-Body Medical Devices," ed: US Patent 20,150,025,503, 2015.
[74] V. Sood, D. Fischer, J. Eklund, and T. Brown, "Developing a communication infrastructure for the smart grid," in Electrical Power & Energy Conference (EPEC), 2009 IEEE, 2009, pp. 1-7.
[75] W. Luan, D. Sharp, and S. Lancashire, "Smart grid communication network capacity planning for power utilities," in Transmission and Distribution Conference and Exposition, 2010 IEEE PES, 2010, pp. 1-4.
[76] H. Farhangi, "The path of the smart grid," Power and Energy Magazine, IEEE, vol. 8, pp. 18-28, 2010.
78
[77] S. M. Amin and B. F. Wollenberg, "Toward a smart grid: power delivery for the 21st century," Power and Energy Magazine, IEEE, vol. 3, pp. 34-41, 2005.
[78] V. C. Gungor, D. Sahin, T. Kocak, S. Ergut, C. Buccella, C. Cecati, and G. P. Hancke, "Smart grid technologies: communication technologies and standards," Industrial informatics, IEEE transactions on, vol. 7, pp. 529-539, 2011.
[79] R. Yu, Y. Zhang, S. Gjessing, C. Yuen, S. Xie, and M. Guizani, "Cognitive radio based hierarchical communications infrastructure for smart grid," Network, IEEE, vol. 25, pp. 6-14, 2011.
[80] M. H. Rehmani, A. C. Viana, H. Khalife, and S. Fdida, "A cognitive radio based internet access framework for disaster response network deployment," arXiv preprint arXiv:1007.0126, 2010.
[81] P. Pawelczak, R. Venkatesha Prasad, L. Xia, and I. G. Niemegeers, "Cognitive radio emergency networks-requirements and design," in New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on, 2005, pp. 601-606.
[82] A. Gorcin and H. Arslan, "Public safety and emergency case communications: Opportunities from the aspect of cognitive radio," in New Frontiers in Dynamic Spectrum Access Networks, 2008. DySPAN 2008. 3rd IEEE Symposium on , 2008, pp. 1-10.
[83] B. L. Malone III, "Wireless search and rescue: Concepts for improved capabilities," Bell Labs Technical Journal, vol. 9, pp. 37-49, 2004.
[84] S. Ghafoor, P. D. Sutton, C. J. Sreenan, and K. N. Brown, "Cognitive radio for disaster response networks: survey, potential, and challenges," Wireless Communications, IEEE, vol. 21, pp. 70-80, 2014.
[85] H. Khayami, M. Ghassemi, K. Ardekani, B. Maham, and W. Saad, "Cognitive radio ad hoc networks for smart grid communications: A disaster management approach," in Communications in China (ICCC), 2013 IEEE/CIC International Conference on , 2013, pp. 716-721.
[86] C. W. Bostian, "The promise of cognitive radio for communications and remote sensing for critical infrastructure, disaster, safety, and risk management," in Radio Science Meeting (USNC-URSI NRSM), 2013 US National Committee of URSI National, 2013, pp. 1-1.
[87] K. Lorincz, D. J. Malan, T. R. Fulford-Jones, A. Nawoj, A. Clavel, V. Shnayder, G. Mainland, M. Welsh, and S. Moulton, "Sensor networks for emergency response: challenges and opportunities," Pervasive Computing, IEEE, vol. 3, pp. 16-23, 2004.
[88] S. Masuda, M. Morita, and S. Nakagawa, "The signal control system with radio in a disaster," in Intelligent Transportation Systems, 1999. Proceedings. 1999 IEEE/IEEJ/JSAI International Conference on, 1999, pp. 594-598.
[89] J. S. Griswold, T. L. Lightle, and J. G. Lovelady, "Hurricane Hugo: effect on state government communications," Communications Magazine, IEEE, vol. 28, pp. 12-17, 1990.
79
[90] B. S. Manoj and A. H. Baker, "Communication challenges in emergency response," Communications of the ACM, vol. 50, pp. 51-53, 2007.
[91] S. Kim, J. Lee, H. Wang, and D. Hong, "Sensing performance of energy detector with correlated multiple antennas," IEEE Signal Processing Letters, vol. 16, p. 671, 2009.
[92] V. R. S. Banjade, N. Rajatheva, and C. Tellambura, "Performance analysis of energy detection with multiple correlated antenna cognitive radio in Nakagami-m fading," Communications Letters, IEEE, vol. 16, pp. 502-505, 2012.
[93] A. Algans, K. I. Pedersen, and P. E. Mogensen, "Experimental analysis of the joint statistical properties of azimuth spread, delay spread, and shadow fading," Selected Areas in Communications, IEEE Journal on, vol. 20, pp. 523-531, 2002.
[94] S. Hiremath, A. K. Mishra, and S. K. Patra, "Engineering Review of the IEEE 802.22 Standard on Cognitive Radio," in White Space Communication, ed: Springer, 2015, pp. 1-31.
[95] K. Ishizu, K. Hasegawa, K. Mizutani, H. Sawada, K. Yanagisawa, T. Keat-Beng, T. Matsumura, S. Sasaki, M. Asano, and H. Murakami, "Field experiment of long-distance broadband communications in TV white space using IEEE 802.22 and IEEE 802.11 af," in Wireless Personal Multimedia Communications (WPMC), 2014 International Symposium on, 2014, pp. 468-473.
[96] P. Gronsund, P. Pawelczak, J. Park, and D. Cabric, "System level performance of IEEE 802.22-2011 with sensing-based detection of wireless microphones," Communications Magazine, IEEE, vol. 52, pp. 200-209, 2014.
[97] Y. Zeng, Y.-C. Liang, A. T. Hoang, and R. Zhang, "A review on spectrum sensing for cognitive radio: challenges and solutions," EURASIP Journal on Advances in Signal Processing, vol. 2010, p. 2, 2010.
[98] M. Subhedar and G. Birajdar, "Spectrum sensing techniques in cognitive radio networks: A survey," International Journal of Next-Generation Networks, vol. 3, pp. 37-51, 2011.
[99] E. A. Lee and D. G. Messerschmitt, Digital communication: Kluwer, 1994.
[100] B. Sklar, Digital communications vol. 2: Prentice Hall NJ, 2001.
[101] M. K. Simon, S. M. Hinedi, and W. C. Lindsey, Digital communication techniques: signal design and detection: Prentice Hall PTR, 1995.
[102] S. Atapattu, C. Tellambura, and H. Jiang, Energy Detection for Spectrum Sensing in Cognitive Radio: Springer, 2014.
[103] V. I. Kostylev, "Energy detection of a signal with random amplitude," in Communications, 2002. ICC 2002. IEEE International Conference on , 2002, pp. 1606-1610.
[104] Y. Zeng, Y.-C. Liang, and R. Zhang, "Blindly combined energy detection for spectrum sensing in cognitive radio," Signal Processing Letters, IEEE, vol. 15, pp. 649-652, 2008.
80
[105] Z. Ye, G. Memik, and J. Grosspietsch, "Energy detection using estimated noise variance for spectrum sensing in cognitive radio networks," in Wireless Communications and Networking Conference, 2008. WCNC 2008. IEEE, 2008, pp. 711-716.
[106] S. Sahoo, T. Samant, A. Mukherjee, and A. Datta, "Advanced Energy Sensing Techniques Implemented Through Source Number Detection for Spectrum Sensing in Cognitive Radio," in Computational Vision and Robotics, ed: Springer, 2015, pp. 179-187.
[107] Z. Tang and T. Peng, "A Wideband Spectrum Data Compression Algorithm base on Energy Detection," Appl. Math, vol. 9, pp. 419-424, 2015.
[108] D. Bhargavi and C. R. Murthy, "Performance comparison of energy, matched-filter and cyclostationarity-based spectrum sensing," in Signal Processing Advances in Wireless Communications (SPAWC), 2010 IEEE Eleventh International Workshop on , 2010, pp. 1-5.
[109] H.-S. Chen, W. Gao, and D. G. Daut, "Spectrum sensing using cyclostationary properties and application to IEEE 802.22 WRAN," in Global Telecommunications Conference, 2007. GLOBECOM'07. IEEE, 2007, pp. 3133-3138.
[110] B. Deepa, A. P. Iyer, and C. R. Murthy, "Cyclostationary-based architectures for spectrum sensing in IEEE 802.22 WRAN," in Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE, 2010, pp. 1-5.
[111] N. Han, S. Shon, J. H. Chung, and J. M. Kim, "Spectral correlation based signal detection method for spectrum sensing in IEEE 802.22 WRAN systems," in Advanced Communication Technology, 2006. ICACT 2006. The 8th International Conference , 2006, pp. 6 pp.-1770.
[112] S. Lim, S. Kim, C. Park, and M. Song, "The detection and classification of the Wireless Microphone signal in the IEEE 802.22 WRAN system," in Microwave Conference, 2007. APMC 2007. Asia-Pacific, 2007, pp. 1-4.
[113] G. Ganesan and Y. Li, "Cooperative spectrum sensing in cognitive radio networks," in New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on, 2005, pp. 137-143.
[114] W. Zhang, R. K. Mallik, and K. Letaief, "Cooperative spectrum sensing optimization in cognitive radio networks," in Communications, 2008. ICC'08. IEEE International Conference on, 2008, pp. 3411-3415.
[115] C. Sun, W. Zhang, and K. Letaief, "Cooperative spectrum sensing for cognitive radios under bandwidth constraints," in Wireless Communications and Networking Conference, 2007. WCNC 2007. IEEE, 2007, pp. 1-5.
[116] J. Ma, G. Zhao, and Y. Li, "Soft combination and detection for cooperative spectrum sensing in cognitive radio networks," Wireless Communications, IEEE Transactions on, vol. 7, pp. 4502-4507, 2008.
[117] B. Shen and K. S. Kwak, "Soft combination schemes for cooperative spectrum sensing in cognitive radio networks," ETRI Journal, vol. 31, pp. 263-270, 2009.
81
[118] M. I. B. Shahid and J. Kamruzzaman, "Weighted soft decision for cooperative sensing in cognitive radio networks," in Networks, 2008. ICON 2008. 16th IEEE International Conference on, 2008, pp. 1-6.
[119] H. Uchiyama, K. Umebayashi, T. Fujii, K. SAKAGUCHI, Y. KAMIYA, and Y. SUZUKI, "Study on soft decision based cooperative sensing for cognitive radio networks," IEICE transactions on communications, vol. 91, pp. 95-101, 2008.
[120] N. A. N. Saad and M. Arshad, "Hard decision fusion based cooperative spectrum sensing in cognitive radio system," Journal of ICT Research and Applications, vol. 3, pp. 109-122, 2009.
[121] H. Rifà-Pous, M. J. Blasco, and C. Garrigues, "Review of robust cooperative spectrum sensing techniques for cognitive radio networks," Wireless Personal Communications, vol. 67, pp. 175-198, 2012.
[122] S. M. Mishra, A. Sahai, and R. W. Brodersen, "Cooperative sensing among cognitive radios," in Communications, 2006. ICC'06. IEEE International Conference on, 2006, pp. 1658-1663.
[123] Z. Quan, S. Cui, and A. H. Sayed, "Optimal linear cooperation for spectrum sensing in cognitive radio networks," Selected Topics in Signal Processing, IEEE Journal of, vol. 2, pp. 28-40, 2008.
[124] J.-S. Pang, G. Scutari, D. P. Palomar, and F. Facchinei, "Design of cognitive radio systems under temperature-interference constraints: A variational inequality approach," Signal Processing, IEEE Transactions on, vol. 58, pp. 3251-3271, 2010.
[125] W. Wang, T. Peng, and W. Wang, "Optimal power control under interference temperature constraints in cognitive radio network," in Wireless Communications and Networking Conference, 2007. WCNC 2007. IEEE, 2007, pp. 116-120.
[126] K. Kim, I. Akbar, K. Bae, J.-S. Um, C. Spooner, and J. Reed, "Cyclostationary approaches to signal detection and classification in cognitive radio," in New frontiers in dynamic spectrum access networks, 2007. DySPAN 2007. 2nd IEEE international symposium on, 2007, pp. 212-215.
[127] W. A. Gardner, "Cyclostationarity in communications and signal processing," DTIC Document1994.
[128] Y. Liu, Z. Zhong, G. Wang, and D. Hu, "Cyclostationary Detection Based Spectrum Sensing for Cognitive Radio Networks," Journal of Communications, vol. 10, 2015.
[129] D. Ghosh and S. Bagchi, "Cyclostationary Feature Detection Based Spectrum Sensing Technique of Cognitive Radio in Nakagami-m Fading Environment," in Computational Intelligence in Data Mining-Volume 2, ed: Springer, 2015, pp. 209-219.
[130] S. Atapattu, C. Tellambura, and H. Jiang, "Energy detection based cooperative spectrum sensing in cognitive radio networks," Wireless Communications, IEEE Transactions on, vol. 10, pp. 1232-1241, 2011.
[131] D. Cabric, A. Tkachenko, and R. W. Brodersen, "Spectrum sensing measurements of pilot, energy, and collaborative detection," in Military Communications Conference, 2006. MILCOM 2006. IEEE, 2006, pp. 1-7.
82
[132] B. Li, M. Sun, X. Li, A. Nallanathan, and C. Zhao, "Energy Detection based Spectrum Sensing for Cognitive Radios over Time-Frequency Doubly Selective Fading Channels," 2015.
[133] R. Tandra and A. Sahai, "Fundamental limits on detection in low SNR under noise uncertainty," in Wireless Networks, Communications and Mobile Computing, 2005 International Conference on, 2005, pp. 464-469.
[134] A. Sonnenschein and P. M. Fishman, "Radiometric detection of spread-spectrum signals in noise of uncertain power," Aerospace and Electronic Systems, IEEE Transactions on, vol. 28, pp. 654-660, 1992.
[135] S. Yarkan, W. Halbawi, and K. A. Qaraqe, "An experimental setup for performance evaluation of spectrum sensing via energy detector: indoor environment," in Proceedings of the 4th International Conference on Cognitive Radio and Advanced Spectrum Management, 2011, p. 42.
[136] Y. Chen, "Improved energy detector for random signals in Gaussian noise," Wireless Communications, IEEE Transactions on, vol. 9, pp. 558-563, 2010.
[137] A. Ghasemi and E. S. Sousa, "Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs," Communications Magazine, IEEE, vol. 46, pp. 32-39, 2008.
[138] T. S. Rappaport, Wireless communications: principles and practice vol. 2: prentice hall PTR New Jersey, 1996.
[139] A. F. Molisch, Wireless communications: John Wiley & Sons, 2007.
[140] C.-W. Ahn and S.-H. Chung, "Enhancing WLAN Performance with Rate Adaptation Considering Hidden Node Effect," International Journal of Distributed Sensor Networks, vol. 2015, 2015.
[141] A. K. Kattepur, A. T. Hoang, Y.-C. Liang, and M. J. Er, "Data and decision fusion for distributed spectrum sensing in cognitive radio networks," in Information, Communications & Signal Processing, 2007 6th International Conference on , 2007, pp. 1-5.
[142] E. Axell, G. Leus, E. G. Larsson, and H. V. Poor, "Spectrum sensing for cognitive radio: State-of-the-art and recent advances," Signal Processing Magazine, IEEE, vol. 29, pp. 101-116, 2012.
[143] C. Sun, W. Zhang, and K. Ben, "Cluster-based cooperative spectrum sensing in cognitive radio systems," in Communications, 2007. ICC'07. IEEE International Conference on, 2007, pp. 2511-2515.
[144] T. C. Aysal, S. Kandeepan, and R. Piesiewicz, "Cooperative spectrum sensing with noisy hard decision transmissions," in Communications, 2009. ICC'09. IEEE International Conference on, 2009, pp. 1-5.
[145] F. Y. Suratman and A. M. Zoubir, "Collaborative spectrum sensing in cognitive radio using hard decision combining with quality information," in Statistical Signal Processing Workshop (SSP), 2011 IEEE, 2011, pp. 377-380.
83
[146] W. Ejaz, N. ul Hasan, S. Lee, and H. S. Kim, "I3S: Intelligent spectrum sensing scheme for cognitive radio networks," EURASIP Journal on Wireless Communications and Networking, vol. 2013, pp. 1-10, 2013.
[147] S. Maleki, A. Pandharipande, and G. Leus, "Two-stage spectrum sensing for cognitive radios," in Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, 2010, pp. 2946-2949.
[148] K. MAWATWAL, "Two stage spectrum sensing for cognitive radio," National Institute of Technology Rourkela, 2014.
[149] S. Geethu and G. L. Narayanan, "A novel high speed two stage detector for spectrum sensing," Procedia Technology, vol. 6, pp. 682-689, 2012.
[150] P. R. Nair, A. P. Vinod, K. G. Smitha, and A. K. Krishna, "Fast two-stage spectrum detector for cognitive radios in uncertain noise channels," IET communications, vol. 6, pp. 1341-1348, 2012.
[151] W.-J. Yue, B.-Y. Zheng, Q.-M. Meng, and W.-J. Yue, "Combined energy detection and one-order cyclostationary feature detection techniques in cognitive radio systems," The Journal of China Universities of Posts and Telecommunications, vol. 17, pp. 18-25, 2010.
[152] N. Kundargi and A. Tewfik, "Hierarchical sequential detection in the context of dynamic spectrum access for cognitive radios," in Electronics, Circuits and Systems, 2007. ICECS 2007. 14th IEEE International Conference on , 2007, pp. 514-517.
[153] R. Chen, J.-M. Park, and K. Bian, "Robust distributed spectrum sensing in cognitive radio networks," in INFOCOM 2008. The 27th Conference on Computer Communications. IEEE, 2008.
[154] R. Chen, J.-M. Park, Y. T. Hou, and J. H. Reed, "Toward secure distributed spectrum sensing in cognitive radio networks," Communications Magazine, IEEE, vol. 46, pp. 50-55, 2008.
[155] J. K. Sreedharan and V. Sharma, "Spectrum sensing using distributed sequential detection via noisy reporting MAC," Signal Processing, vol. 106, pp. 159-173, 2015.
[156] L. Lai, Y. Fan, and H. V. Poor, "Quickest detection in cognitive radio: A sequential change detection framework," in Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008. IEEE, 2008, pp. 1-5.
[157] N. Kundargi and A. Tewfik, "Sequential pilot sensing of ATSC signals in IEEE 802.22 cognitive radio networks," in Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on , 2008, pp. 2789-2792.
[158] A. Jayaprakasam and V. Sharma, "Sequential detection based cooperative spectrum sensing algorithms in cognitive radio," in Cognitive Wireless Systems (UKIWCWS), 2009 First UK-India International Workshop on, 2009, pp. 1-6.
[159] Q. Zou, S. Zheng, and A. H. Sayed, "Cooperative spectrum sensing via sequential detection for cognitive radio networks," in Signal Processing Advances in Wireless Communications, 2009. SPAWC'09. IEEE 10th Workshop on, 2009, pp. 121-125.
84
[160] K. W. Choi, W. S. Jeon, and D. G. Jeong, "Sequential detection of cyclostationary signal for cognitive radio systems," Wireless Communications, IEEE Transactions on, vol. 8, pp. 4480-4485, 2009.
[161] Q. Zou, S. Zheng, and A. H. Sayed, "Cooperative sensing via sequential detection," Signal Processing, IEEE Transactions on, vol. 58, pp. 6266-6283, 2010.
[162] M. Seif, M. Karmoose, and M. Youssef, "Censoring for Improved Sensing Performance in Infrastructure-less Cognitive Radio Networks," arXiv preprint arXiv:1502.01986, 2015.
[163] N. Mansoor, A. M. Islam, M. Zareei, S. Baharun, and S. Komaki, "Cluster Modelling for Cognitive Radio Ad-hoc Networks Using Graph Theory," in International Conference on Applied Mathematics, Modelling and Simulation (ICAMMS2014), 2014.
[164] N. Shirke, K. Patil, S. Kulkarni, and S. Markande, "Energy efficient cluster based routing protocol for distributed Cognitive Radio Network," in Networks & Soft Computing (ICNSC), 2014 First International Conference on, 2014, pp. 60-65.
[165] L. De Nardis, D. Domenicali, and M. Di Benedetto, "Clustered hybrid energy-aware cooperative spectrum sensing (CHESS)," in Cognitive Radio Oriented Wireless Networks and Communications, 2009. CROWNCOM'09. 4th International Conference on, 2009, pp. 1-6.
[166] N. Nguyen-Thanh and I. Koo, "A cluster-based selective cooperative spectrum sensing scheme in cognitive radio," EURASIP Journal on Wireless Communications and Networking, vol. 2013, pp. 1-9, 2013.
[167] S. Hussain and X. Fernando, "Approach for cluster-based spectrum sensing over band-limited reporting channels," Communications, IET, vol. 6, pp. 1466-1474, 2012.
[168] A. C. Malady and C. R. da Silva, "Clustering methods for distributed spectrum sensing in cognitive radio systems," in Military Communications Conference, 2008. MILCOM 2008. IEEE, 2008, pp. 1-5.
[169] G. Xie and A. A. Polo, "SYSTEMS AND METHODS FOR IMPLEMENTING BLUETOOTH LOW ENERGY COMMUNICATIONS," ed: US Patent 20,150,049,871, 2015.
[170] J.-R. Lin, T. Talty, and O. K. Tonguz, "On the potential of bluetooth low energy technology for vehicular applications," Communications Magazine, IEEE, vol. 53, pp. 267-275, 2015.
[171] P. Bhagwat, "Bluetooth: technology for short-range wireless apps," Internet Computing, IEEE, vol. 5, pp. 96-103, 2001.
[172] N. Erasala and D. C. Yen, "Bluetooth technology: a strategic analysis of its role in global 3G wireless communication era," Computer Standards & Interfaces, vol. 24, pp. 193-206, 2002.
[173] B. Zhen, Y. Kim, and K. Jang, "The analysis of coexistence mechanisms of Bluetooth," in Vehicular Technology Conference, 2002. VTC Spring 2002. IEEE 55th , 2002, pp. 419-423.
85
[174] N. Golmie, N. Chevrollier, and O. Rebala, "Bluetooth and WLAN coexistence: challenges and solutions," Wireless Communications, IEEE, vol. 10, pp. 22-29, 2003.
[175] O. A. Bamahdi and S. A. Zummo, "An Adaptive Frequency Hopping TechniqueWith Application to Bluetooth-WLAN Coexistence," in Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies, 2006. ICN/ICONS/MCL 2006. International Conference on , 2006, pp. 131-131.
[176] W. Hu, D. Willkomm, M. Abusubaih, J. Gross, G. Vlantis, M. Gerla, and A. Wolisz, "Cognitive radios for dynamic spectrum access-dynamic frequency hopping communities for efficient ieee 802.22 operation," Communications Magazine, IEEE, vol. 45, pp. 80-87, 2007.
[177] C. Cordeiro, K. Challapali, D. Birru, and N. Sai Shankar, "IEEE 802.22: the first worldwide wireless standard based on cognitive radios," in New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on, 2005, pp. 328-337.
[178] S. J. Shellhammer, "Spectrum sensing in IEEE 802.22," IAPR Wksp. Cognitive Info. Processing, pp. 9-10, 2008.
[179] C. Cordeiro, M. Ghosh, D. Cavalcanti, and K. Challapali, "Spectrum sensing for dynamic spectrum access of TV bands," in Cognitive Radio Oriented Wireless Networks and Communications, 2007. CrownCom 2007. 2nd International Conference on, 2007, pp. 225-233.
[180] Y. Youn, H. Jeon, J. H. Choi, and H. Lee, "Fast spectrum sensing algorithm for 802.22 WRAN systems," in Communications and Information Technologies, 2006. ISCIT'06. International Symposium on, 2006, pp. 960-964.
[181] H. Kim and K. G. Shin, "In-band spectrum sensing in IEEE 802.22 WRANs for incumbent protection," IEEE Transactions on Mobile Computing, pp. 1766-1779, 2010.
[182] S. Sengupta, S. Brahma, M. Chatterjee, and N. Sai Shankar, "Enhancements to cognitive radio based IEEE 802.22 air-interface," in Communications, 2007. ICC'07. IEEE International Conference on, 2007, pp. 5155-5160.
[183] M. Höyhtyä, A. Hekkala, M. D. Katz, and A. Mämmelä, "Spectrum awareness: techniques and challenges for active spectrum sensing," in Cognitive wireless networks, ed: Springer, 2007, pp. 353-372.
[184] K. S. Challapali and J. Wang, "Channel management method in a distributed spectrum cognitive radio network," ed: Google Patents, 2013.
[185] H. Wang, G. Noh, D. Kim, S. Kim, and D. Hong, "Advanced sensing techniques of energy detection in cognitive radios," Communications and Networks, Journal of, vol. 12, pp. 19-29, 2010.
[186] R. Tandra and A. Sahai, "SNR walls for signal detection," Selected Topics in Signal Processing, IEEE Journal of, vol. 2, pp. 4-17, 2008.
86
[187] A. Taherpour, M. Nasiri-Kenari, and S. Gazor, "Multiple antenna spectrum sensing in cognitive radios," Wireless Communications, IEEE Transactions on, vol. 9, pp. 814-823, 2010.
[188] A. Singh, M. R. Bhatnagar, and R. K. Mallik, "Cooperative spectrum sensing in multiple antenna based cognitive radio network using an improved energy detector," Communications Letters, IEEE, vol. 16, pp. 64-67, 2012.
[189] D. Raman and N. Singh, "Improved Threshold Scheme for Energy Detection In Cognitive Radio Under Low SNR," Proc. of Int. Conf. on Emerging Trends in Engi-neering and Technology 2013.
[190] J. K. Tugnait, "On multiple antenna spectrum sensing under noise variance uncertainty and flat fading," Signal Processing, IEEE Transactions on, vol. 60, pp. 1823-1832, 2012.
[191] P. D. Sutton, K. E. Nolan, and L. E. Doyle, "Cyclostationary signatures in practical cognitive radio applications," Selected Areas in Communications, IEEE Journal on, vol. 26, pp. 13-24, 2008.
[192] J. Lundén, V. Koivunen, A. Huttunen, and H. V. Poor, "Collaborative cyclostationary spectrum sensing for cognitive radio systems," Signal Processing, IEEE Transactions on, vol. 57, pp. 4182-4195, 2009.
[193] A. Pandharipande and J.-P. Linnartz, "Performance analysis of primary user detection in a multiple antenna cognitive radio," in Communications, 2007. ICC'07. IEEE International Conference on, 2007, pp. 6482-6486.
[194] T. Wimalajeewa and P. K. Varshney, "Polarity-coincidence-array based spectrum sensing for multiple antenna cognitive radios in the presence of non-Gaussian noise," Wireless Communications, IEEE Transactions on, vol. 10, pp. 2362-2371, 2011.
[195] B. Zayen and A. Hayar, "Dimension estimation based detector for multiple-antenna cognitive radio networks," in Telecommunications (ICT), 2012 19th International Conference on, 2012, pp. 1-4.
[196] K. Arshad and K. Moessner, "Mobility driven energy detection based spectrum sensing framework of a cognitive radio," in Cognitive Wireless Systems (UKIWCWS), 2010 Second UK-India-IDRC International Workshop on, 2010, pp. 1-5.
[197] R. M. Gray, "Toeplitz and circulant matrices: A review," Communications and Information Theory, vol. 2, pp. 155-239, 2005.
[198] Z. Lei and S. J. Shellhammer, "IEEE 802.22: The first cognitive radio wireless regional area network standard," IEEE communications magazine, vol. 47, pp. 130-138, 2009.
[199] A. R. Van Erkel and M. Peter, "Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology," European Journal of radiology, vol. 27, pp. 88-94, 1998.
[200] A. Ghasemi and E. S. Sousa, "Asymptotic performance of collaborative spectrum sensing under correlated log-normal shadowing," Communications Letters, IEEE, vol. 11, pp. 34-36, 2007.
87
[201] A. Ghasemi and E. S. Sousa, "Collaborative spectrum sensing for opportunistic access in fading environments," in New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on , 2005, pp. 131-136.
[202] Y. Chen and N. C. Beaulieu, "Performance of collaborative spectrum sensing for cognitive radio in the presence of Gaussian channel estimation errors," Communications, IEEE Transactions on, vol. 57, pp. 1944-1947, 2009.
[203] W. Zhang, R. K. Mallik, and K. Letaief, "Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks," Wireless Communications, IEEE Transactions on, vol. 8, pp. 5761-5766, 2009.
[204] H. Li, X. Cheng, K. Li, C. Hu, N. Zhang, and W. Xue, "Robust collaborative spectrum sensing schemes for cognitive radio networks," Parallel and Distributed Systems, IEEE Transactions on (Volume:25 , Issue: 8 ) 2013.
[205] W. Wang, H. Li, Y. Sun, and Z. Han, "CatchIt: detect malicious nodes in collaborative spectrum sensing," in Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE, 2009, pp. 1-6.
[206] L. Duan, A. W. Min, J. Huang, and K. G. Shin, "Attack prevention for collaborative spectrum sensing in cognitive radio networks," Selected Areas in Communications, IEEE Journal on, vol. 30, pp. 1658-1665, 2012.
[207] J. Meng, W. Yin, H. Li, E. Hossain, and Z. Han, "Collaborative spectrum sensing from sparse observations in cognitive radio networks," Selected Areas in Communications, IEEE Journal on, vol. 29, pp. 327-337, 2011.
[208] L. Zhao, G. Liu, J. Chen, and Z. Zhang, "Flooding and directed diffusion routing algorithm in wireless sensor networks," in Hybrid Intelligent Systems, 2009. HIS'09. Ninth International Conference on, 2009, pp. 235-239.
[209] M. Gudmundson, "Correlation model for shadow fading in mobile radio systems," Electronics letters, vol. 27, pp. 2145-2146, 1991.
[210] N. Rakesh and S. Srivatsa, "An Investigation on Propagation Path Loss in Urban Environments for Various Models at Transmitter Antenna Height of 50 m and Receiver Antenna Heights of 10 m, 15 m and 20 m respectively," International Journal of Research & Reviews in Computer Science, vol. 3, 2012.
[211] R. A. Horn and C. R. Johnson, Matrix analysis: Cambridge university press, 2012.
[212] J. A. Wood, T. W. Miller, and D. S. Hargrove, "Clinical Supervision in Rural Settings: A Telehealth Model," Professional Psychology: Research and Practice, vol. 36, p. 173, 2005.
[213] W. H. Organization, Telemedicine: opportunities and developments in Member States: report on the second global survey on eHealth : World Health Organization, 2010.
[214] R. N. Anderson and N. C. f. H. Statistics, "Deaths: leading causes for 2001," National Vital statistical Reports 2003.
[215] A. S. Cacciapuoti, I. F. Akyildiz, and L. Paura, "Primary-user mobility impact on spectrum sensing in cognitive radio networks," in Personal Indoor and Mobile Radio
88
Communications (PIMRC), 2011 IEEE 22nd International Symposium on , 2011, pp. 451-456.
[216] L. De Nardis and M.-D. Guirao, "Mobility-aware design of cognitive radio networks: challenges and opportunities," in Cognitive Radio Oriented Wireless Networks & Communications (CROWNCOM), 2010 Proceedings of the Fifth International Conference on, 2010, pp. 1-5.
[217] A. W. Min and K. G. Shin, "Impact of mobility on spectrum sensing in cognitive radio networks," in Proceedings of the 2009 ACM workshop on Cognitive radio networks, 2009, pp. 13-18.
[218] H. Sun, A. Nallanathan, C.-X. Wang, and Y. Chen, "Wideband spectrum sensing for cognitive radio networks: a survey," Wireless Communications, IEEE, vol. 20, pp. 74-81, 2013.
[219] C.-H. Hwang, G.-L. Lai, and S.-C. Chen, "Spectrum sensing in wideband OFDM cognitive radios," Signal Processing, IEEE Transactions on, vol. 58, pp. 709-719, 2010.
[220] A. Taherpour, S. Gazor, and M. Nasiri-Kenari, "Wideband spectrum sensing in unknown white Gaussian noise," IET communications, vol. 2, pp. 763-771, 2008.
[221] M. Derakhtian, F. Izedi, A. Sheikhi, and M. Neinavaie, "Cooperative wideband spectrum sensing for cognitive radio networks in fading channels," Signal Processing, IET, vol. 6, pp. 227-238, 2012.
[222] Z. Tian and G. B. Giannakis, "BER sensitivity to mistiming in ultra-wideband impulse radios-part II: fading channels," Signal Processing, IEEE Transactions on, vol. 53, pp. 1897-1907, 2005.
[223] Z. Quan, S. Cui, H. V. Poor, and A. H. Sayed, "Collaborative wideband sensing for cognitive radios," Signal Processing Magazine, IEEE, vol. 25, pp. 60-73, 2008.
[224] L. Khalid, K. Raahemifar, and A. Anpalagan, "Cooperative spectrum sensing for wideband cognitive OFDM radio networks," in Vehicular Technology Conference Fall (VTC 2009-Fall), 2009 IEEE 70th, 2009, pp. 1-5.
[225] T.-H. Yu, O. Sekkat, S. Rodriguez-Parera, D. Markovic, and D. Cabric, "A wideband spectrum-sensing processor with adaptive detection threshold and sensing time," Circuits and Systems I: Regular Papers, IEEE Transactions on, vol. 58, pp. 2765-2775, 2011.
[226] D. Datla, R. Rajbanshi, A. M. Wyglinski, and G. J. Minden, "An adaptive spectrum sensing architecture for dynamic spectrum access networks," Wireless Communications, IEEE Transactions on, vol. 8, pp. 4211-4219, 2009.
[227] Z. Quan, S. Cui, A. H. Sayed, and H. V. Poor, "Wideband spectrum sensing in cognitive radio networks," in Communications, 2008. ICC'08. IEEE International Conference on, 2008, pp. 901-906.
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Vita
Aamir Zeb Shaikh is working as an Assistant Professor in the Telecommunications Engi-
neering Dept. of NED University, along with doing research towards his PhD degree in
spectrum sensing algorithms for cognitive radios under the supervision of Dr. Shoaib Hasan
Zaidi, Dean Engineering and Sciences at Habib University and co-supervision of Dr. Talat
Altaf, professor and Dean ECE at NED University of Engineering & Technology, Karachi. He
is engaged in research/ project supervision since 2002. During 2011, he also worked as a re-
search fellow at Electrical Engineering Dept. of the University of Texas at Dallas, USA, where
he developed a novel architecture for future cognitive radio enabled telemedicine systems un-
der the supervision of Professor Dr. Lakshman Tamil, professor at Erik Johansson School,
UTDallas, TX. He was also the in-charge of PHS/WLL Exchange Laboratory at NED Univer-
sity. He earned his BE and ME Electrical Engineering with focus on Telecommunications in
2001 and 2005, respectively, from the same university where he is serving.