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EED310 PROJECT REPORT

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Aim

1. To understand cooperative communication and analyze SER

performance for DF cooperative signaling.

2. To understand the working of Software Defined Radio (SDR).

Submitted by:

Siddhartha Das

(Entry No.: 2009EE10418)

Neeraj Yadav

(Entry No.: 2009EE10400)

Supervisor

Prof. Manav Bhatnagar

Indian Institute of Technology Delhi

May 2012

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Acknowledgements

It is our pleasure to record deepest gratitude to our Supervisor Dr. Manav Bhatnagar forgiving us this opportunity to work under his supervision and his interest and valuablesuggestions. We would like also to thank the previous members of the GNU Radio Lab

and all the students working in the lab for their constant support and cooperationthroughout this project.

Siddhartha Das (2009EE10418)

Neeraj Yadav (2009EE10400)

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Contents

Part One

Abstract

1. Cooperative Communication1.1 Cooperative Signaling Methods

1.1. a Detect and Forward Methods 1.1. b Amplify and Forward Methods

1.2. Essentials of Cooperative Communication 1.2.1 Problems andAssumptions 1.2.2 Opportunities

2. SER performance analysis for a DF cooperative signaling

2.1 System Model 2.2 SER Performance Analysis

2.2. a Closed Form SER Formulations 2.2. b Upper Bound SERFormulations 2.2. c Outage Probability

3. Applications

Part Two

Abstract

1. Software Defined Radio

1.1 GNU-Radio 1.2 USRP2

1.2.1 RFX2400 Daughterboard

2. Experimental Set-up

2.1 Transmitter Implementation 2.2 Packet Message 2.3 RRC Filter 2.4Receiver

References

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PART ONE

To understand cooperative communication and analyze SERperformance for DF cooperative signaling.

ABSTRACT Here we try to give a brief idea what cooperative communication is allabout, in what way is it better, and why should we use it. The basic idea is that single-antenna mobiles in a multi-user scenario can share their antennas in a manner thatcreates a virtual MIMO system. The mobile wireless channel suffers from fading,meaning that the signal attenuation can vary significantly over the course of a giventransmission. Transmitting independent copies of the signal generates diversity and caneffectively combat the deleterious effects of fading. In particular, spatial diversity isgenerated by transmitting signals from different locations, thus allowing independentlyfaded versions of the signal at the receiver. Cooperative communication generates thisdiversity in a new and interesting way.

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1. COOPERATIVE COMMUNICATION

For a preliminary explanation of the ideas behind cooperative communication, we referto Fig. 1. This figure shows two mobile agents communicating with the samedestination. Each mobile has one antenna and cannot individually generate spatial

diversity. However, it may be possible for one mobile to receive the other, in which caseit can forward some version of overheard information along with its own data. Becausethe fading paths from two mobiles are statistically independent, this generates spatialdiversity.

Figure 1

In cooperative wireless communication, we are concerned with a wireless network, ofthe cellu-lar or ad hoc variety, where the wireless agents, which we call users, mayincrease their effective quality of service (measured at the physical layer by bit errorrates, block error rates, or outage probability) via cooperation. In a cooperative

communication system, each wireless user is assumed to transmit data as well as actas a cooperative agent for another user (Fig. 2).

Cooperation leads to interesting trade-offs in code rates and transmit power. In the caseof power, one may argue on one hand that more power is needed because each user,when in cooperative mode, is transmitting for both users. On the other hand, thebaseline transmit power for both users will be reduced because of diversity. In the faceof this trade-off, one hopes for a net reduction of transmit power, given every-thing elsebeing constant.

Similar questions arise for the rate of the sys-tem. In cooperative communication each

user transmits both his/her own bits as well as some information for his/her partner; onemight think this causes loss of rate in the system. However, the spectral efficiency ofeach user improves because, due to cooperation diversity the channel code rates canbe increased. Again a trade-off is observed.

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Figure 2

One may also describe cooperation as a zero-sum game in terms of power andbandwidth of the mobiles in the network. The premise of cooperation is that certain(admittedly unconventional) allocation strategies for the power and bandwidth of

mobiles lead to significant gains in system performance. In the cooperative allocation ofresources, each mobile transmits for multiple mobiles.

1.1 COOPERATIVE SIGNALING METHODS

We now review several of the main cooperative signaling methods. A simplifieddemonstration and comparison of these methods appears in Fig. 3

1.1.a Detect and Forward Methods

This method is perhaps closest to the idea of a traditional relay. In this method a userattempts to detect the partners bits and then retransmits the detected bits (Fig. 3). Thepartners may be assigned mutually by the base station, or via some other technique.We consider two users partnering with each other, but in reality the only important factoris that each user has a partner that provides a second (diversity) data path. The easiestway to visualize this is via pairs, but it is also possible to achieve the same effect viaother partnership topologies that remove the strict constraint of pairing. Partnerassignment is a rich topic of research.

This signaling has the advantage of simplicity and adaptability to channel conditions.Several notes must be made in reference to this method. First, it is possible thatdetection by the partner is unsuccessful, in which case cooperation can be detrimentalto the eventual detection of the bits at the base station. Also, the base station needs to

know the error characteristics of the inter user channel for optimal decoding.

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Figure 3

1.1.b Amplify and Forward Methods

Another simple cooperative signaling is the amplify-and-forward method. Each user inthis method receives a noisy version of the signal transmitted by its partner. As the

name implies, the user then amplifies and retransmits this noisy version. The basestation combines the information sent by the user and partner, and makes a finaldecision on the transmitted bit (Fig.3). Although noise is amplified by cooperation, thebase station receives two independently faded versions of the signal and can makebetter decisions on the detection of information.

In amplify-and-forward it is assumed that the base station knows the inter user channelcoefficients to do optimal decoding, so some mechanism of exchanging or estimatingthis information must be incorporated into any implementation. Another potentialchallenge is that sampling, amplifying, and retransmitting analog values is

technologically nontrivial. Nevertheless, amplify-and-forward is a simple method thatlends itself to analysis, and thus has been very useful in furthering our understanding ofcooperative communication systems.

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1.2 ESSENTIALS OF COOPERATIVE COMMUNICATION

1.2.1 Problems and Assumptions

Cooperative communication, as described previously, assumes that the base stationcan separately receive the original and relayed transmissions. This is accomplished by

transmitting the two parts orthogonally so that they can be separated. The moststraightforward method is separation in time, that is, the users data and relayed dataare transmitted in non-overlapping time intervals.

It is also important to consider the knowledge required by the base station to handlecooperative communication. The amount of additional information varies for the variousschemes introduced previously. In the simple detect-and-for -ward method, the basestation needs to know the error probability of the inter user channel for optimaldetection. In amplify-and-forward this is not required, since conventional channelestimation methods can be used to extract the necessary information from the direct

and relayed signals.In the course of the development of cooperative communication, several complicatingissues must be addressed, including the loss of rate to the cooperating mobile, overallinterference in the network, cooperation assignment and hand -off, fairness of thesystem, and transmit and receive requirement on the mobiles.

1.2.2 Opportunities

One may ask what the tangible benefits of cooperation are at the network level. Toanswer this, we point to the multi-antenna technologies that motivated cooperation inthe first place. Studies have shown that the diversity provided by MIMO space-time

codes can improve performance at the medium access control (MAC), network, andtransport layers. Since the net effect of cooperation in a micro-scattering environment,in terms of bit and packet error rates, is similar to that of space- time codes (bothprovide spatial diversity), one can use the same studies to conclude that cooperationcan provide the same advantages as MIMO space-time codes in the higher layers.

An important question is how partners are assigned and managed in multi-usernetworks. In other words, how is it determined which users cooperate with each other,and how often are partners reassigned? Systems such as cellular, in which the userscommunicate with a central base station, offer the possibility of a centralized

mechanism. Assuming that the base station has some knowledge of the all thechannels between users, partners could be assigned to optimize a given performancecriterion, such as the average block error rate for all users in the network. In contrast,systems such as ad hoc networks and sensor networks typically do not have anycentralized control. Such systems therefore require a distributed cooperative protocol, inwhich users are able to independently decide with whom to cooper-ate at any giventime. A related issue is the extension of the proposed cooperative methods to allow a

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user to have multiple partners. The challenge here is to develop a scheme that treats allusers fairly, does not require significant additional system resources, and can beimplemented feasibly in conjunction with the systems multiple access protocol. Anotherimportant issue is the development of power control mechanisms for cooperativetransmission. Work thus far generally assumes that the users transmit with equal power.

It may be possible to improve performance even further by varying transmit power foreach user.

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2. SER PERFORMANCE ANALYSIS FOR A DECODE-AND-FORWARD

COOPERATIVE SIGNALING

We consider a decode-and-forward cooperation protocol in wireless networks. We sawclosed-form symbol-error-rate (SER) for the decode-and-forward cooperation systems

with PSK and QAM signals. Since the closed-form SER formulation is complicated, weestablished two SER upper bounds to show the asymptotic performance of thecooperation system, in which one of them is tight at high signal-to-noise ratio (SNR).

2.1 SYSTEM MODEL

A cooperation strategy has been considered with two phases in a wireless network. InPhase 1, each mobile user (or node) in the wireless network sends information to itsdestination, and the information is also received by other users at the same time. InPhase 2, each user helps others by decoding the information that it received from otherusers in Phase 1 and sending out the decoded symbols. In both phases, all users

transmit signals through orthogonal channels by using TDMA, FDMA or CDMA scheme.For better understanding the cooperation concept, the focus is on a two- usercooperation scheme. Specifically, user 1 sends information to its destination in Phase 1,and user 2 also receives the information. User 2 decodes the information and helpsuser 1 to send out the information in Phase 2. Similarly, when user 2 sends itsinformation to its destination in Phase 1, user 1 receives and decodes the informationand will send it to user 2s destination in Phase 2. Due to the symmetry of the two users,only user 1s performance will be analyzed. Without loss of generality, we will consider aconcise model as shown in Fig. 4, in which source denotes user 1 and relay representsuser 2.

Figure 4

In Phase 1, the source broadcasts its information to both the destination and the relay.The received signals y

s,d

and y

s,r

at the destination and the relay respectively can be written as

(1) and (2)

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in which P

1

is the transmitted power at the source, x is the transmitted information symbol, and

s,d and

s,r

are additive noise. In (1) and (2), h

s,d

and h

s,r are the channel coefficients from thesource to the destination and the relay respectively.

If the relay is able to decode the transmitted symbol correctly, then in Phase 2, the relayforwards the decoded symbol with power P

2

to the destination, otherwise the relay does not send oridle. Thus, the received signal at the destination in Phase 2 can be modeled as

(3)

Where P

2

= P

2

if the relay decodes the transmitted symbol correctly, otherwise P

2

=0, and h

r,dis the channel coefficient from the relay to the destination. The channel coefficients h

s,d,

h

s,r and h

r,d are modeled as zero-mean, complex Gaussian random variables with variances

2

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,drespectively. They are assumed to be known at the receiver, but not at the transmitter.The noise terms

2

s,d,

2

s,r

and

s,d,

s,r

and

r,d

are modeled as zero-mean complex Gaussian random variableswith variance N

0 .

Jointly combining the received signal from the source directly in Phase 1 and that fromthe relay in Phase 2, the destination detects the transmitted symbols by use ofthemaximum-ratio combining (MRC) . We fix the total transmitted power P such as

P

1

= P. (4)

Note that the power saving in case of P

2

+ P

2

=0 is negligible, since at high SNR, the chancethat the relay incorrectly decodes the symbol is rare.

2.2 SER PERFORMANCE ANALYSIS

In this section, we analyzed the SER performance for the cooperative communicationsystems. We derived closed-form SER formulations for the systems with M -PSK and M

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QAM modulation, and also provide two SER upper bounds to reveal the asymptoticperformance.

2.2. a Closed-form SER formulations

With knowledge of the channel coefficients h

s,d

(from the source to the destination) and h

r,d(from the relay to the destination), the output of the detector at the destination can bewritten as

(5)

Where a

1

=P1

h*s,d

/ N

0

and a

2

=P2

h*r,d

/ N

0

. Assume that the transmitted symbol xhas average energy 1, then the SNR of the output is

(6)