Long-term adaptation solutions do not receive much attention in the design phase of a wireless system. A new approach is proposed, where the antenna takes an active role in characterising and learning the operation environment. The proposed solution is based on a signal fishing mechanism. Several software components, among which a genetic optimizer, implement the processing stages of autonomous design of the antenna array during operation

Cremene, Ligia Chira

Cremene, Marcel

Crisan, Nicolae

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Int. J. of Computers, Communications & Control, ISSN 1841-9836, E-ISSN 1841-9844Vol. VI (2011), No. 1 (March), pp. 63-71Software Components for Signal Fishing based on GA ElementPosition OptimizerN. Crisan, L.C. Cremene, M. CremeneNicolae Crisan, Ligia Chira Cremene,Marcel CremeneTechnical University of Cluj-Napoca Romania,Cluj-Napoca, Memorandumului nr. 28, 400114E-mail(s): Nicolae.Crisan@com.utcluj.ro,Ligia.Cremene@com.utcluj.ro,Marcel.Cremene@com.utcluj.roAbstract: Long-term adaptation solutions do not receive much attention inthe design phase of a wireless system. A new approach is proposed, wherethe antenna takes an active role in characterising and learning the operationenvironment. The proposed solution is based on a signal ﬁshing mechanism.Several software components, among which a genetic optimizer, implement theprocessing stages of autonomous design of the antenna array during operation.Keywords: smart antennas, signal ﬁshing, long-term dynamic adaptation,GA optimizer1 IntroductionWireless receivers incorporate more and more adaptive techniques in the attempt to com-pensate the eﬀects of the radio channel. Technical challenges for current wireless technologiesinclude limited bandwidth and transmit power, interference, signal fading and other propagationimpairments.A multitude of adaptive techniques are nowadays implemented at diﬀerent levels of a wirelesstransmission chain, as depicted in ﬁgure 1. At the physical layer, data link and network layersthere are many adaptive techniques that deal with modulation and coding schemes, equalization,ﬁltering, ARQ mechanism, fragmentation, routing [9], [10]. These techniques have more or lessindependent approaches, and their joint operation is not much studied. Adaptive solutions areusually speciﬁc solutions, having a local eﬀect. Most of the existing adaptive solutions are hard-coded and therefore not able to evolve.The main concerns regarding wireless receiver performance are link availability and link ca-pacity. Tens of adaptive techniques are trying to dynamically adapt the transmission to thechanges in the wireless environment. These are short-term, instant adaptation methods/mech-anisms. Long-term adaptation solutions are more complex and their design and operation aretime consuming.The majority of the adaptation methods considering the spatial component of the propa-gation channel use highly-parameterized, yet simpliﬁed channel models [3], [4]. Usually ﬁxedparameters, such as mean values of statistical channel parameters are involved [5]. Also, process-ing methods that consider time and frequency variability of the channel exploit only partiallythe spatial component information.An approach for developing a long-term dynamic adaptation solution for wireless receiverchains is proposed. Unlike current approaches the antenna takes an active role in characterisingand learning the operation environment, namely the wireless propagation channel.The proposed solution is based on a spatial characterization of the fading allowing the de-tection of signal maxima in the antenna environment. The antenna is described as an MxNCopyright c 2006-2011 by CCC Publications64 N. Crisan, L.C. Cremene, M. Cremenerectangular array. Only two elements in the array are active at a given time. In order to searchfor the best positions of the active elements a genetic optimizer is used.This paper is organized as follows: Section 2 discuses the interdisciplinary aspect of thesmart antenna ﬁeld. In section 3 we describe several software components that implement theprocessing stages of the autonomous design of the antenna array during operation. The proposedsolution is based on a signal ﬁshing mechanism [6] and uses a genetic optimizer. Experimentalresults are presented in section 4 together with our conclusions.Figure 1: Adaptive techniques operating at diﬀerent levels of the wireless Tx-Rx chain2 Smart antenna interdisciplinary aspectClassical smart antennas are based on antenna arrays of diﬀerent conﬁgurations and con-trolled by means of a specialized signal processor. Their beamforming and beamsteering mech-anisms address capacity and reliability issues of wireless systems [14], [15]. The subject ofsmart antennas, also known as adaptive antennas, becomes a widely interdisciplinary ﬁeld as itbridges several disciplines: electronics, electromagnetics, channel&propagation modelling, com-munication techniques – signal processing, control&adaptive systems, random processes, andevolutionary computing.Antenna, communications, and control engineers tend to view adaptive antennas from quitediﬀerent perspectives. Some are inclined to focus on electromagnetic features (e.g. radiationpatterns and levels), others on communication-link parameters like SNR (Signal-to-Noise Ratio)and BER (Bit Error Rate), and others on various control algorithms.There are at least three issues that computational intelligence (especially evolutionary com-puting) can help solve in smart antenna systems. The ﬁrst direction is generating new, optimizedantenna geometries, capable of multi-mode operation. The other two are dynamic optimizationissues that concern signal detection and tracking and statistical signal processing.We identify the antenna as a pivotal element in determining and assessing quality in wire-less communication systems. Actually, the user-perceived network or equipment quality relies,ultimately, on antenna performance. Moreover, until recently, the antenna inﬂuence on channelmeasurements for channel modelling was considered a bias, now it can be used to a beneﬁt byintegrating it into the channel analysis [3], [4].The distance between transmitter and receiver, their speed, their transmit powers, obstacles,all have an impact on the channel matrix [7], [2], [1]. These are diﬃcult to control factors. Yet,there is a system that has a great inﬂuence on the channel matrix, a system that is under thecontrol of the receiver: it is the receiver antenna array. The position of the antenna array inSoftware Components for Signal Fishing based on GA Element Position Optimizer 65the ﬁeld and the spacing between array elements are the main parameters that can inﬂuence thechannel matrix.3 Proposed solution: Signal ﬁshing based on GA element positionoptimizerSignal ﬁshing (SF) is a new concept [6] that addresses fading mitigation in adaptive multiple-antenna receivers. The SF mechanism is based on spatial fading characterization relaying on theantenna array itself. The main idea is to detect and exploit channel signal maxima.Fading is a random ﬂuctuation of amplitude and phase of the propagated signal. Amplitudeﬂuctuation in a multipath environment is usually described by a Rayleigh distribution. A usefulfading characterization will turn the selective-fading channel problem into a ﬂat-fading problem,and will enable the receiver signal processing part to perform better [6].Antenna selection techniques generally consider a ﬁxed number of elements in the antennaarray allowing only regular conﬁgurations. Signal ﬁshing allows a random activation of a variablenumber of array elements which is equivalent to dynamically modifying the element spacing dand the orientation angle .Figure 2 a) illustrates the description of the receive rectangular array which is part of atransmit-receive system operating in a MIMO 2x2 conﬁguration. Each element is a Sierpinskigasket (Fig. 2b). Two elements are selected at a speciﬁc moment of time and six wave-frontsare considered. The two array elements operate as receive antennas; the other elements areused for channel estimation and spatial fading characterization and are uniformly spaced (d isconstant only for these Np-2 elements). Spatial fading characterization involves estimation ormeasuring of the angle of arrival i„ phase shift 'i, and amplitude ai of the incoming signal whichis performed during the training sequence. When the training sequence elapses, the channel isconsidered quasi-static for about 448 symbols. By changing the element displacement (elementperturbation) we can introduce a phase diﬀerence between arriving signals Rx1 and Rx2.Figure 2: a) 32x32 rectangular antenna array  = 900, 0    900, dx = dy = 4 , M = N = 32.b) Sierpinski gasket multi-band antennaThe signal equations for each of the two receive elements, Rx1 and Rx2, are:Rx1 (a; b) =npXi=1(i;900)fiexp [j ((a  1) (dxcos i )) + j (b  1) (dysin i )] (1)Rx2 (c; d) =npXi=1(i;900)fiexp [j ((c  1) (dxcos i )) + j (d  1) (dysin i )]66 N. Crisan, L.C. Cremene, M. Cremenewhere:  is the antenna element factorfi = jfij exp(ji) is the complex value associated to wavefront i.a, b, c, and d are the coordinates of the activated receive elements Rx1 and Rx2 is the phase constanti is the angle of arrival (AoA) of the ith front.Figure 3 presents an illustration of the signal ﬁshing concept – exploiting the spatial compo-nent information of the channel in order to detect and use the signal maxima. Signal maximaare estimated based on the optimum element spacingdopt = PNpi=1 jhij 4K14siniPNpi=1 jhij(2)where K is a positive integer; the array grows wider with K.Figure 3: Signal ﬁshing with two antennas                                   Figure 4: Signal ﬁshing based on GA element position optimizerFigure 4 shows the block diagram of the signal ﬁshing mechanism realization. The main com-ponents are: an Np-element antenna array, two receiver chains, an array controller, a basebandprocessing component and a genetic optimizer.The array controller plays a key role in the antenna selection mechanism. The array controllercan be located between the antenna array and the receiver RF chain. Antenna selection controlis based on a switching algorithm that activates two elements at a time. The array controlleris the software component that translates the coordinates a, b, c, d into a binary signal suitedfor on/oﬀ element switching. This is done via a microcontroller located on a Software DeﬁnedRadio board. This algorithm can be implemented in the context of the SDAA (Software DeﬁnedAutonomous Antenna), described by the authors in [11].Software Components for Signal Fishing based on GA Element Position Optimizer 67Beneﬁts of adaptive antenna spacing and angle diversity are discussed in [16]. Passive an-tenna diversity alone is not enough to ensure signiﬁcant capacity improvements in MIMO sys-tems. However, spacing and angle reconﬁguration of the antenna array is enough to signiﬁcantlyincrease the system data rate [8]. Classical digital beamforming and beamsteering algorithmscan achieve this but with limited degrees of freedom and introducing noise. Activating theappropriate two array elements results in maximization of Rx1 and Rx2 signals.Activating certain elements in the array is equivalent to complex coeﬃcient multiplication(complex weighting) in baseband processing [2]. This spatial method is meant to maximize theSNR by spatial decorrelation of the incoming fronts.From the adaptive mechanism point of view, the baseband processing component includes twoimportant software components: the PER (Packet Error Rate) calculation component and theRxx correlation matrix estimation component.The PER component counts the dropped packets and statistically evaluates the packet errorrate after thousands of received data packets. This software component is available in mostwireless equipments and its value is readable using a software register of the SDR. The PERvalue is used as a trigger for the genetic search of array element coordinates a,b,c and d.The Rxx component provides the correlation matrix at the receiver; the matrix is not readilyavailable and is computed based on pilot signal information. The Rxx correlation matrix of thereceiver (MxM) oﬀers the mean power delay proﬁle and power angular proﬁle. The H channelmatrix coeﬃcients and angles of arrival (AoA) can be further extracted.Rxx = E_x_Hx= ARSSAH +Rnn (3)where E is the expectation function and_Hx is the Hermitian of the received signal matrix x (Mx1 )M is the number of elements that are used for power and angle proﬁle estimationA is the array of the steering vectors (MxD) (one vector for each angle of arrival, D propa-gation paths are considered and D<M ).RSS is the corellation matrix of the source (DxD) andRnn is the correlation matrix of the noise (MxM ).Computation of the H matrix is implemented in many wireless standards, such as IEEE802.11n, 802.16e. The H matrix represents the average energy of the channel between receiveantenna j and transmit antenna i. The angles of arrival are estimated based on the availableRxx matrix, using the ESPRIT method [1]. The computation time is not critical in this casebecause Rxx computation is also a statistical problem. Rxx computation is an ergodic processthat depends on the signal-to-noise ratio.GA element-position optimizer. Genetic Algorithms (GAs) entered the electromagnetic do-main in the 1990s, and have been successfully used in antenna design to evolve new antennageometries [12], [13]. Unlike most approaches, where GAs are involved, our proposal takes intoaccount solution search during device operation. The GA search is based on spatial fadingcharacterisation data.The signal ﬁshing (SF) adaptive mechanism needs to ﬁnd the maximum of the mean receivedsignal power P = jh11j2 + jh22j2, after the MRC signal processing. Therefore the SF mechanismproblem reduces to the maximization of the objective function SF : (MxN)2  > R+, where:SF (a; b; c; d) = jRx1(a; b)j2 + jRx2(c; d)j2 (4)SF (a; b; c; d)! max68 N. Crisan, L.C. Cremene, M. CremeneSolution encoding. Candidate solutions are encoded as x = (a,b,c,d), where a, b ,c ,d areexpressed as binary strings.Fitness assignment. The ﬁtness of solution x is:eval(x) = 1=SF (a; b; c; d) (5)Population model. A generational model where the oﬀspring completely replace the parentpopulation at each generation is considered.Stopping criterion. The search process stops when the maximum number of generations isreached.Search operators. Recombination is based on uniform crossover operator. A mask is randomlygenerated for each pair. One bit mutation is considered.Parameter setting. Population size – we consider the initial population of 100 chromosomesrandomly generated for the ﬁrst generation. Number of generations – between 100 and 300generations. Mutation rate – 0.01. The number of mutations is 19 for each generation and iscomputed [13] with:nrmut = mutrate  nbits  nvar  (npop  1) (6)where: mutrate is the mutation rate (0,01),nbits is the number of bits/chromosome (5),nvar is the number of genes for each chromosome (4 - a,b,c,d) andnpop is the total population number (100 chromosomes).nrmut is the number of mutations per generation.The search process follows the ﬂowchart described in ﬁgure 5.                  Figure 5: GA optimizer ﬂowchart4 Experimental resultsFigure 6 a) illustrates the GA search process. The solution pair is circled in red - Rx1 andRx2. It indicates the best coordinates (a,b) for receiving element Rx1 and (c,d) for Rx2. Thecorresponding array elements to be activated are shown in ﬁgure 6 b). The evolution of theobjective function for each generation is tracked in Figure 7 a), revealing many local maxima.The global maximum is marked in ﬁgure 7 a).Software Components for Signal Fishing based on GA Element Position Optimizer 69Figure 6: a) GA search process and solution, b) corresponding activated array elements Figure 7: a) Objective function over generations, b) Signal-ﬁshing mechanism gain70 N. Crisan, L.C. Cremene, M. CremeneFrom the antenna point of view the local maxima are also important because each can resultin a signal gain. So whenever a tradeoﬀ between speed and gain is necessary these local maximacan be used. The gain is calculated as the diﬀerence between the mean signal power for a ﬁxedtwo-element array and the mean signal power for the signal ﬁshing mechanism.5 ConclusionsA long-term adaptation solution for wireless receivers is proposed. It is based on a systemicapproach of the wireless receiver chain and gives the antenna array a central role in learningthe environment, namely the wireless propagation channel. An implementation of the proposedsignal-ﬁshing concept using a GA optimizer for element positioning shows that the maximumsignal levels of the channel can be detected and used to increase the mean received signal power.The channel fading eﬀects are mitigated starting at the antenna level, an approach that does notintroduce additional noise like current baseband processing techniques.AcknowledgmentsThis work was supported by CNCSIS-UEFISCSU, PNII-IDEI, project number 1062/2007.Bibliography[1] F. Gross, Smart antennas for wireless communications, McGraw Hill, 2005[2] A.B. Gershman, N.D. Sidiropoulos, Space-Time Processing for MIMO Communications, Wi-ley&Sons, 2005[3] 3GPP, “SpatialChannel Model for Multiple Input Multiple Output MIMO simulations,” Tech-nical Speciﬁcation Group Radio Access Network TR 25.996 v6.1.0, 3GPP, Sept. 2003.[4] J. Salo, G. Del Galdo, J. Salmi, et al., “MATLAB implementation of the 3GPP spatial channelmodel,” Tech. Rep. TR 25.996, 3GPP, Jan. 2005.[5] L. Mucchi, Claudia Staderini, J. Ylitalo, P. Kyosti, “Modiﬁed Spatial Channel Model forMIMO Wireless Systems”, EURASIP J. on Wireless Comm. and Netw., vol. 2007, pp.1-7.[6] N. Crisan, Ligia C. Cremene, “Antenna-based Signal Fishing”, The Fifth Int. Conf. on Wire-less and Mobile Communications – ICWMC’09, pp.152-156, IEEE Computer Society Press,Cannes, 2009.[7] G. Tsoulos, MIMO System Technology for Wireless Communications, CRC Press Cityplace-Boca Raton, StateFlorida, 2006.[8] N. Crisan, Ligia C. Cremene, "A Novel Combining Technique for Adaptive Antenna Arrays",ACTA TECHNICA NAPOCENSIS Electronics and Telecommunications, vol. 49/2, pp.27-34,Mediamira Science Publisher, Cluj-Napoca, 2008.[9] M-S. Alouini, Adaptive and Diversity Techniques for Wireless Digital Communications overFading Channels, Ph.D. Thesis, California Institute of Technology, 1998.[10] L. Hanzo, C. H. Wong, M. S. Yee, Adaptive Wireless Transceivers, John Wiley & Sons, NY,2002.Software Components for Signal Fishing based on GA Element Position Optimizer 71[11] Ligia C. Cremene, N. Crisan, “Towards Cognitive Antenna Systems based on Antenna-Channel Co-evolution” 3rd Int. Workshop on Soft Computing Applications - SOFA2009,pp.165-169, Szeged, 2009.[12] D. Dumitrescu, B. Lazzerini, L. C. Jain, A. Dumitrescu, Evolutionary computation, CRCPress, NY, 2000.[13] R. L. Haupt, Sue Ellen Haupt, Practical genetic algorithms, John Wiley & Sons, Inc., 2004.[14] S. D. Blostein, H. Leib, “Multiple Antenna Systems: Their Role and Impact în FutureWireless Access”, IEEE Communications Magazine, vol.41, no.7, pp.94-101, July 2003.[15] Angeliki Alexiou, M. Haardt, “Smart Antenna Technologies for Future Wireless Systems:Trends and Challenges”, IEEE Communications Magazine, vol.42, no.9, pp. 90-97, 2004.[16] J. D. Boerman, J. T. Bernhard, “Performance Study of Pattern Reconﬁgurable Antennas inMIMO Communication Systems”, IEEE Trans. on Ant. and Propag., Vol.56, No.1, Jan.2008.

Software Components for Signal Fishing based on GA Element Position Optimizer

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Software Components for Signal Fishing based on GA Element Position Optimizer

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