abstract - ijoart...into smart antenna system. using the human auditory system as an analogy, it...

International Journal of Advancements in Research & Technology, Volume 4, Issue 7, July -2015 51 ISSN 2278-7763

Copyright © 2015 SciResPub. IJOART

ABSTRACT

The paper focuses on the interaction and integration of several critical components of a mobile communication network using smart-antenna systems. This wireless network is composed of communicating nodes that are mobile, and its topology is continuously changing. One of the central motivations for this work comes from the observed dependence of the overall network throughput on the design of the adaptive antenna system and its underlying signal processing algorithms. In the framework of control methods for adaptive phased-arrays, this paper deals with complex communication scenarios by considering a memory-enhanced cooperative algorithm. Compared to existing approaches where far-field interferences are taken into account, the proposed analysis considers a more realistic situation where the jamming sources are located either in the near-field or in the far-field of the receiving antenna. Key Words: Smart antenna, memory-enhanced cooperative algorithm, MIMO, PSO The demand for increased capacity in wireless network has motivated recent research towards the development of algorithms and standards that exploit space selectivity [1]. As a result, there are many efforts on the design of “Smart” antenna arrays and the associated beam forming algorithms[2][3]. Smart-antenna systems provide opportunities for higher system. The continuous evolution of communication systems requires the development and customization of techniques based on the idea of diversity [5]. In the frame work of antennas design, such a theory has been applied for developing smart systems able to

MEMORY ENHANCED SMART ANTENNAS FOR WiRElESS COMMUNiCATiON SANTOSH KUMAR JHA

ASSiSTANT PROFESSOR, ECE DEPT FET, MODY UNivERSiTY,lAKSHMANgARH

RAJASTHAN – 332311 [email protected]

INTRODUCTION

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improve the quality of the received signal and to suppress the effects of interfering sources. The concept of spatial diversity [6] has led to the coupling of array theory with adaptive control and therefore to the design of antenna architectures able to maximize the system performances [i.e., the signal-to-interference-plus-noise ratio (SINR) by tuning dynamically the weights of the array elements. The mathematical theory of adaptive systems has been originally proposed by Applebaum in [7] dealing with linear arrays of isotropic sources in the presence of far-field (FF) narrow-band signals (i.e. a desired signal and a set of jammers). The array weights, adapted for placing nulls in the far-field pattern in the directions of interference, are obtained by multiplying the quiescent weights by the inverse of the sampled covariance matrix formed from the complex signals received at each element in the array. Alternatively, the adaptive control has been also recast as an optimization problem by defining a suitable cost functional to be maximized. Originally, deterministic techniques based on gradient methods (e.g., the least mean square (LMS) algorithm by widrow et al. [8][9] have been proposed, but the resulting approaches were still characterized by several non-negligible drawbacks. Because of the need of estimating the covariance matrix of the desired signal from the measurements of the received signals at each element of the array, the array must have an expensive receiver or a correlator at each element. Unfortunately, most arrays (or the simplest/cheapest) have a single receiver at the output of the summer and the receivers (when available) would require sophisticated calibrations. On the other side, these methods consider variable analog amplitude and phase weights, but phased arrays usually have only digital beam steering phase shifters at the elements and the feed network (fixed) determines the amplitude values. Therefore, the continuous phase values calculated by the adaptive algorithms are only approximated and the quantization error limits the null placement. In order to reduce the complexity and the costs of adaptive systems, the possibility of implementing a phase-only control (i.e., adjusting the phase shift resetting) for reducing the total output

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power measured by the receiver at the output of the summer has been investigated. A significant improvement on this technique has been proposed by Haupt [10] who used a genetic algorithm (GA) to adjust some of the least significant bits of the beam steering phase shifters for minimizing the total output power thus removing the interfering signals from the output of the array. Notwithstanding the success and successive experimental implementation, such a (GA)-based approach did not take into account constantly changing conditions and the need of a readaptation to new environments once the population converged. Such a problem has been overcome in successive works by weile and Michielssen [11] or Donelli et al [12] by using dipoloidy and dominance or cooperative algorithms (i.e., the particle swarm optimizer (PSO). In this paper in the initial state the paper presents an insight into smart antenna system. Using the human auditory system as an analogy, It introduces the different types of smart antenna systems. The paper is aimed at assessing the effectiveness and reliability of an enhanced PSO-BASED technique in the presence of more complex working conditions. In particular, the signals impinging on the array are characterized by randomly variable directions and generated by electromagnetic sources located at differences distances from the antenna system. More in detail, the source of the desired signal is assumed to be very far from the system, whereas the distance of the interfering sources from the array varies from the near to the far zone. Such a situation turns out to be quite realistic since it could model/describe an “info-mobility” scenario where a moving network node (e.g., a car or a pedestrian) communicates with a base station, a neighboring node (i.e., close to the receiving system) would be considered as a near-field jamming source.

The functionally of many engineering system is readily understood when it is related to our human-body system. Therefore, to give an insight of how a smart-antenna system works. Let’s imagine two persons carrying on a conversation inside a pitch-dark room (Figure1). The listener among the two persons is capable of

SMART ANTENNA ANALOGY

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determining the location of the speaker as he or she moves about the room because the voice of the speaker arrives at each acoustic sensor – the – ear – at a different time. The human signal processor – the brain – computes the direction of the speaker from the time differences or delays received by the two ears. Afterwards, the brain adds the strength of the signals from each ear, so as to focus on the sound of the computed direction. Furthermore, if additional speakers join in the conversation, the brain can tune out unwanted interferers and concentrate on one conversation at a time. Fig 1: Respond of the listener (human being) and Respond of the smart antenna

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Conversely, the listener can respond back to the same direction as the desired speaker by orienting his or her transmitter – his or her mouth – towards the speaker. Electrical smart-antenna systems work the same way, using many antennas instead of the ears, and a digital signal processor instead of the brain (Figure 1). Therefore after the digital signal processor receives the time delays from each antenna element, it computes the direction-of-arrival (DOA) of the signal of interest (SOI). It then adjust the excitations (the amplitudes and phase of the signals) to produce a radiation pattern that focuses on the signal of interest SOI, while tuning out any signal not of interest (SNOI). Maintaining capacity has always been a challenge, as the number of services and subscribers increases. To achieve the capacity demand required by a growing number of subscribers, cellular radio systems had to evolve throughout the years. To justify the need for smart-antenna system in the current cellular system structure, a brief history on the evolution of cellular radio systems is present. Since the early days, system designers knew that capacity would be a problem, especially because the number of channels or frequencies allotted by the Federal Communication Commission (FCC) was limited. Therefore, to achieve the capacity required for thousands of subscribers, a suitable cellular structure had to be designed; the resulting structure is depicted in Figure – 2. Each shaded hexagonal area in Figure-2 represents a small geographical area, called a cell with a maximum range (radius) R. At the center of each cell resides a base station equipped with an omni directional antenna. With a given band of frequencies, Base stations in adjacent cells are assigned frequency bands that contain frequencies completely different from neighboring cells. By limiting the coverage area to within the boundaries of a cell, which are separated from one another by distances large enough to keep interference levels within a system is called frequency reuse cells having the same side use the same frequency spectrum. Cell splitting, as shown in Figure – 3, subdivides a congested cell into smaller cells, called microcells, each with its own base station and a corresponding reduction in antenna height and transmitter power. Cell splitting improves capacity by decreasing the cell radius R, and keeping the D/R ratio unchanged; D is the distance between the centers of the clusters. The disadvantages of cell splitting are the costs incurred from the installation of new base stations, the increase in the number of handoffs (the process of transferring communication to a new base station when the mobile unit

THE QUEST FOR MORE CAPACITY

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travels from one cell to another), and a higher processing load per subscriber. Despite it benefits, cell-sectoring did not provide the solution needed for the capacity problem, therefore the system designers began to look into a system that could dynamically satirize a cell. Hence, they began to examine smart antennas. Fig 2: A typical cellular structure with a seven cells reuse pattern Fig 3: A schematic diagram of cell splitting Many refer to smart-antenna systems as smart antennas, but in reality, antennas are not smart; it is the digital signal processing, along with the antennas which make the system smart. Although it might seem that smart-antenna systems are a new technology, the fundamental theory of smart antennas is not new. In fact, they have been applied in defense-related systems since world war – II. In recent years, with the emergence of powerful, low-cost digital signals processors (DSPs). Geneal-purpose

SMART ANTENNA SYSTEMS

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processors (and application-specific integrated circuits, or ASICs) as well as innovative signal-processing algorithms, smart-antenna systems have become practical for commercial us. Smart-antenna systems are basically an extension of cell sectoring in which the sector coverage is composed of multiple beams. This is achieved by the use of antenna arrays, and the number of beams in the sector (e.g., a sector of 120°) is a function of the array geometry. Because smart antennas can focus their radiation pattern toward the desired users while rejecting all unwanted interference and therefore, a lower bit error rate (BER) they can provide a substantial capacity improvement. These systems can generally be classified as either swathed-beam or adaptive-array systems. A switched-beam system is a system that can choose from one of many predefined patterns, in order to enhance the received signal (Figure-4). It is obviously an extension of cell-sectoring, as each sector is subdivided into smaller sectors. As the mobile unit movies throughout the cell, the switched-beam systems detects the signal strength, choose the appropriate predefined beam pattern, and continually switches the beams as necessary. The overall goal of the switched-beams system is to increase gain, according to the location of the user. However, since the beams are fixed, the intended user may not be in the center of the main beam. If there is an interfere near the center of the active beam, it may by enhance more than the desired user. Adaptive-array systems can locate and track signals and can dynamically adjust the antenna pattern to enhance reception while minimizing interference, using signal processing algorithms. A functional block diagram of such a system is shown in figure – 5. This figure shows that after the system down-converts the received signals to base band and digitizes them, it locates the signal of interest using the direction of arrival (DOA) algorithm computes the direction of arrival of all signals by computing the time delays among the antenna elements. Afterwards, the adaptive algorithm, physical communications channel in the same cell, with only an angular separation. This technology dramatically improves the interference-suppression capability while it greatly increase frequency cost. Basically, capacity is increased not only through inter-cell frequency reuse, but also through

SWITCHED-BEAM SYSTEMS

ADAPTIVE-ARRAY SYSTEM

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intra-cell frequency reuse.

Fig 4: A switched beam system

Let us consider an array of N elements. The narrowband signal received by the nth element of the array at the time-step² te, e =1,…, L, can be expressed as follows:

L1,...., l 1,.....N;n e )(t a )(t s(r)nj

e(r)

e(r)n === α

------------------- (1)

where )(t h ,e )(t h )(t erftj2

e(r)

e)( eπ=ra and f being the slowly-varying

envelope of the received signal and the carrier frequency, respectively. Moreover, )(r

n℘ is the phase term of the received signal coming from the angular coordinates ),( rr Φθ that identify the direction-of-arrival )( ADΩ of the received signal.Under far-field conditions [13], the phase term of (1) turns out to be

)(2nrnrnr

rn zqyvxu ++=

λπχ

------------- (2)

Where ru = rr rrr rr cos q and ,øsin , sin v,ø cos , sin θθθ == and (xn, yn, zn) are the Cartesian coordinates of the nth element of the array.

MATHEMATICAL FORMULATION

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By considering Co-channel interferences )(rns is the result of the

summation of the desired signal )(dns , a set of I jammers

Fig 5: A typical architecture of Adaptive array

..... 1, i ; )(, =gnis , and an uncorrelated background noise for noise signal

)(ons ] characterized by an average power to

2σ

(3) ------ )(t )(ts )t()(t

I

1 i

(g)ni,

)(33

on

dn

rn sss ++= ∑

=

Where )t( dns =

dnjd eta α )()(

and )(

,)(t )(t )(,

)(,

gnijg

nigni eas α

=

Analogously to (2), ),(u ) / (2 d)(

nzdndnd

n qyvx ++= λπα

222)(, )()()([2 niiiiniigni zupyvpxuppi −+−+−−=

λπα ]

n = 1,….., N; I = 1,……. I --------------- (4) To model [16] the phase term of the ith interference source located at (pi,ɵi, øi) either in the far-field depending on the value of pi. As far as the signal )(es available at the output of the summer is concerned, it appears that

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(5) ----- - )(1 )(t

)(

e)(8∑

=

=N

n nee

rn

WtS

Where njenn ww β= is the nth complex weight. Consequently, the total

output power measured by the single receiver is equal to

∑ ∑= =≈

Ω∆= −−

N

ne

N

p

rnp

ppte twwwptp pjenje

1 1. (6) ------- )()()( 3β

That is a function of )( 1,....N, n ; , er

npn tww Ω== being the (p,n)-entry of the convariance matrix of the received signal. In order to minimize the total output power thus removing the interfering signals from the output of the array, the array coefficients are iteratively updated for taking into account constantly changing (i.e., at each time-step) conditions and the need of a readaptation to new environments. Moreover, a time-varying phase-only control is implemented to reduce the complexity and the costs of the adaptive system. In particular, the following optimization problem:

(7) ------ )]([min arg )( eeopt tpt ββ =

Is solved by means of the enhanced PSO-based strategy (section III) to determine the optimal setting of the phase. ,( ,....1: Nnn == ββ since amplitude coefficients .....1: Nnwn = are fixed quantities (e.g., uniform amplitudes or distributed according to Dolph-Chebychev pattern) The principal reason for the growing interest in smart antenna system is the capacity increase. In densely populated areas, mobile systems are normally interference-limited, meaning that the interference from other users is the main source of noise in the system. This means that the signal-to-interference ratio (SIR) is much larger than the signal-to-noise ratio(SNR). In general, smart antennas will increase the SIR by simultaneously increasing the useful received signal level and lowering the interference level. Another benefit that smart-antenna systems provide is an increase of range. Because smart antennas are more directional than omnidirectional and sectorized antennas, a range-increase potential is available. In other words, smart antennas are able to focus their energy toward the intended users,

SMART ANTENNAS BENEFITS

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instead of wasting it in other unnecessary directions, like the omnidirectional antennas. This means that base stations can be placed further apart, leading to a more cost-efficient development. Therefore, smart-antenna systems will be most suited in rural and sparsely populated areas, where radio coverage rather than capacity is more important [4]. Another added advantage of smart-antenna systems is security in a society that is becoming more dependent on conducting business and transmitting personal information, security is an important issue. Smart antennas make it more difficult to tap a connection. Because the intruder must be positioned in the same direction as the user as seen from the base station to successfully tap a connection.

Finally, due to the spatial-detection nature of smart-antenna systems, they can be used to locate people in case of emergencies, or for any other location-specific service. Smart antennas not gained traction with wireless Network operators. Many research and development activities have been poured into developing smart antennas for wireless communications systems. Yet despite the promise of increased network capacity and enhanced spectrum utilization, smart antenna system have largely failed to break into the mainstream cellular networks, as operators have backed at adopting these technologies. In such conditions, MIMO techniques appear to be more suitable, since they rely on a rich propagation channel to increase system capacity. MIMO systems are based on using multiple antennas with uncorrelated signals, along with channel information to pack more bits into the communication channel. Instead of a large, single panel that beam-switching and some adaptive antenna systems require, a number of antennas separated in space can be used. This is similar to the current configuration of cellular base stations, where most sectors feature at least two antennas for diversity reception. In short, MIMO techniques address many of the logistical issues that have limited the deployment of earlier “smart antenna” systems. The expected growth in broadband-data wireless systems is the likely catalyst that will spur the deployment of smart antenna systems. Bandwidth remains the basic requirement for achieving high data rates, despite all the improvements made in access technologies, error-control coding, modulation techniques, and physical and medium access control layer performance. Provide that smart antenna systems can address the issues of cost, integration, simplicity of implementation, and demonstrable and quantifiable improvements, their deployment is only a matter of time.

CONCLUSIONS

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Insight into smart-antenna systems, using the human auditory system as an analogy, as well as a general overview of smart antennas. Moreover, the quest for more capacity was illustrated with the evolution of the cellular radio system. This paper has investigated both the theoretical and numerical aspects of the use of digital phase-shifters only weighting for adaptive null steering in complex interference scenarios. It has demonstrated the application of a PSO-based control equipped with enhanced memory features for the adaptation of the antenna array to minimize the total output power at the receiver. The mathematical formulation of the approach and the algorithmic sequence of the enhanced adaptive control have been carefully described. The numerical validation has been carried out by considering different array geometries and various interference configurations.

1. R.H.Roy, “ An overview of smart Antenna Technology: The Next Wave in Wireless Communications” 1998 IEEE Aerospace Conference (Volume 3), May 1998, pp. 339-345.

2. J.Razavilar Rashid-Farrokhi and K.J.R. Liu, “Traffic Improvements in Wireless Communication Networks using Antenna Array, “ IEEE Journal on Selected Areas in Communications, 18, March 2000, pp. 458-471.

3. P.Rashid-Farokhi, L.Tassiulas and K.J.R.Liu, “ Joint Optimal Power Control and Beamforming in Wireless Networks using Antenna Array, “ IEEE Transactions on Communications, CGM-46, October 1998, pp 1313-1324.

4. A.O.Boukalov and S.G. Haggman, “ System Aspects of smart Antenna Technology in Cellular Wireless Communication.

5. C.B. DIETRICH, w.l.Stutzman, K.Byung-Ki, and K.Dietze, “ Smart antennas in wireless communications: Base-station diversity and handset beamforming, “IEEE Antennas propag. Mf., vol. 52. pp-142-151, oct. 2000.

6. M.Chryssomallis, “Smart antennas,” IEEE Antenna propag. Mag., vol. 42 pp. 129-136, jun. 2000

7. S.P.Applebaum, “ Adaptive arrays,” IEEE Trans. Antennas Propag., vol. 24. pp. 585-598, Sep. 1976.

8. B.widrow, P.E.Mantey, L.J.Griffiths, and B.B.Goode,” Adaptive antenna systems.” Proc. IEEE, vol. 55, pp. 2143-2159, Dec. 1967.

9. L.C.Godara, “ improved LMS algorithm for adaptive beamforming,”IEEE Trans. Antennas Propag., vol. 38 pp. 1631-1635. Oct 1990.

10. R.L.Haupt, “Phase only adaptive sidelobe nulling using digitally controlled phase-shifters,” IEEE Trans. Antennas Propag., vol. 24, pp. 638-649, Sep. 1976

11. D.S.Weile and E.Michielssen, “ The control of adaptive antenna arrays with genetic algorithms using dominance and diploidy,” IEEE Trans. Antenna Propag., vol. 49 pp. 1424-1433, Oct 2001.

References

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12. M.Donelli, R.Azaro, F.D.Natale and A.Massa, “ An innovative computational approach based on a particle swarm strategy for adaptive phased-arrays control,” IEEE Trans. Antennas Propag., vol 54, pp. 888-898. Mar 2006.

13. C.A.Balanis, Antenna Theory, Newyork : wiley, 1996

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abstract - ijoart...into smart antenna system. using the human auditory system as an analogy, it...

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