A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single-input multiple-output (SIMO) systems. The joint ML optimization of the channel and data estimation is decomposed into an iterative optimization loop. An efficient global optimization algorithm termed as the repeated weighted boosting aided search is employed first to identify the unknown SIMO channel model, and then the Viterbi algorithm is used for the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used for demonstrating the efficiency of this joint ML optimization scheme designed for blind adaptive SIMO systems

Chen, S.

Hanzo, L.

Yang, X.C.

English

Southampton (e-Prints Soton)

201Blind Joint Maximum Likelihood Channel Estimation and Data Detection forSingle-Input Multiple-Output SystemsS. Chen, X.C. Yang and L. HanzoSchool of Electronics and Computer ScienceUniversity of Southampton, Southampton S017 1BJ, U.K.Abstract-A blind adaptive scheme is proposed for joint EM-type algorithm, which iteratively performs channelmaximum likelihood (ML) channel estimation and data estimation and symbol detection.detection of single-input multiple-output (SIMO) systems.The joint ML optimization of the channel and data This paper develops a blind adaptive joint maximumestimation is decomposed into an iterative optimization likelihood (ML) channel estimation and data detectionloop. An efficient global optimization algorithm termed as scheme for the SIMO system considered. The proposedthe repeated weighted boosting aided search is employedfirst to identify the unknown SIMO channel model, and X algorith deopesthe intooptimizationoofmthe then the Viterbi algorithm is used for the maximum likeli- channel and data estimation into an iterative optimiza-hood sequence estimation of the unknown data sequence. tion loop by combining a global optimization method,A simulation example is used for demonstrating the referred to as the repeated weighted boosting searchefficiency of this joint ML optimization scheme designed technique (RWBS) [11] invoked for the optimal esti-for blind adaptive SIMO systems. mation ofthe SIMO channel and the Viterbi algorithm(VA) for the maximum likelihood sequence estimationofthe transmitted data sequence. Specifically, first, theI. INTRODUCTION RWBS algorithm [11] searches the channel parameterThe single-input multiple-output (SIMO) system con- space to optimize the ML criterion. Then the VAsisting of a single-antenna transmitter and a receiver decodes the data based on the given channel modelequipped with multiple antennas has enjoyed popularity and feeds back the corresponding likelihood metric toowing to its simplicity. A space-time equalizer (STE) the RWBS algorithm. The efficiency of this joint MLbased on this SIMO structure is capable of mitigating estimation scheme invoked for blind equalization ofthethe channel impairments arising from hostile multipath SIMO system is demonstratedby a simulationexample.propagation. For the sake of improving the achievable We point out that a genetic algorithm (GA) can be usedsystem throughput, employing blind adaptation of the instead of the RWBS algorithm to optimize the SIMOSTE is attractive, since this avoids the reduction of channel estimate. In this case the proposed schemethe effective throughput by invoking training. Blind becomes an extension ofthejoint ML channel and dataspace-time equalization of the SIMO system can be estimation scheme using the GA originally developedperformed by directly adjusting the STE's parameters for the single-input single-output (SISO) system [12].using a constant modulus algorithm (CMA) type adap-tive scheme [1]-[3]. The attainable blind space-time II. THE PROPOSED BLIND JOINT ML ESTIMATIONequalization performance can be further improved upon ALGORITHMaiding the CMA by a soft decision-directed scheme[4]. The research of blind adaptive SIMO systems Consider the SIMO system employing a single trans-has also been focused on blind channel identification mitter antenna and L (> 1) receiver antennas. The[5]-[7]. Once the SIMO channel impulse responses symbol-rate sampled antennas' outputs xi(k) 1 < I <(CIRs) have been identified, various designs, such L, are given byas the minimum mean square error or minimum bit n-1error rate requires [8] can be invoked for the STE. xl(k)= E ci,js(k -i)+ r(k) (1)Alternatively, the decoupled weighted iterative least =squares with projection (DW-ILSP) algorithm [9],[1O] where rl(k) is the complex-valued Gaussian whitecan be adopted. The DW-ILSP algorithm is abatch noiseassociatedwiththelthchannelandE[ ru(k) 21=The financial support of the EPSRC, UK and the EU in the no,{() stetasitdsmo euneadiframework of the NEWCOM, NEXWAY and PHOENIX projects is assumed to take values from the quadrature phase shiftgratefully acknowledged. keying (QPSK) symbol set {± i j}, while c,i, are the202TABLE I is given in the Appendix. The proposed blindjoint ML THE SIMULATED SIMO SYSTEM. optimization scheme can now be summarized.Channel impulse response1 0.365-0.274j 0.730+0.183j -0.440+0.176j Outer level Optimization. The RWBS algo-2 0.278+0.238j -0.636+0.104j 0.667-0.074j rithm searches the SIMO channel parameter3 -0.639+0.249j -0.517-0.308j 0.365+0.183j4 -0.154+0.693j -0.539-0.077j 0.268-0.358j space to find the globally optimal estimate c*by minimizing the mean square error (MSE)CIR taps associated with the Ith receive antenna. For JMSE(d) JML(d, §*) (9)notational simplicity, we have assumed that each ofthe Inner level optimization. Given the channelL channels has the same length of nm. Let estimate c, the VA provides the ML de-coded data sequence s*, and feeds back theX = [1(1) xi(2) ... xi(N) X2(1) ... corresponding value of the likelihood metricXLL(1) XL(2) ... XL(N)]T (2) JML(d, S*) to the upper level.s [s(-nc + 2) s(0) s(1) s(N)]T (3) Let CVA be the complexity of the VA required toc [cO,1 Cl,i ... Cnc_,- CO,2 ... decode a data sequence of(N x L) samples, and denoteCO,L C1,L ...Cn,-l,L] (4) the total number of VA calls required for the RWBSalgorithm to converge by NVA. The complexity of thebe the vector of (N X L) received signal samples, the proposed scheme may be estimated as (NVA x CVA).corresponding transmitted data sequence and the vector The RWBS algorithm is a simple yet efficient globalofuthe SIMO CIRS, respectively. The probability density search algorithm. In several global optimization appli-function of the received data vector x conditioned on cations investigated in [11], including the blind jointthe SIMO CIR C and the symbol vector s is ML channel estimation and data detection in the contextp(x c, s) of the SISO system, the RWBS algorithm achieved aN L 2 similar convergence speed as the GA and was seen to1 2 - Z[ x2,(k)-Z171 c=,s( -i)l be more accurate after convergence to its minimum(2wu2)NL than the GA. The RWBS algorithm has additional(5) advantages ofrequiring a minimum programming effortThe joint ML estimate of c and s is obtained byjointly and having a ?? number ofalgorithmic parameters thatmaximizing p(x c, s) over c and s. Equivalently, the have to be set.joint ML estimate is the minimum ofthe cost functionN L n,-1 2 III. SIMULATION EXAMPLEJMIL(C, =N E xEi(k) iO The number of receive antennas was L = 4, in our(6) simulations while, the transmitted data symbols werenamely QPSK modulated, and the SIMO CIRs listed in Table Iwere used. The length ofdata samples was N = 50. In(c*, s*) = arg [Mn JML(C, S) (7) practice, the value ofthe likelihood metric JMSE(d) is6,§ all that the upper level optimizer has access to, and theThe joint minimization process (7) can also be solved convergence of the algorithm can only be quantifiedusing an iterative loop first over the data sequences s in terms of the MSE (9). The performance of theand then over all the possible channels c: algorithm may also be assessed in terms of the meanF X tap error defined as(*s*)=arg [mmI (IninJML(C, )]) (8)MT=||Cac| (l)The inner or lower-level optimization can readily be wherecarried out using the standard VA. In order to guarantee.Aa joint ML estimate, the search algorithm used in -1+1, if c -cthe outer or upper-level optimization should be capa- a > - f c+c (11)ble of efficiently identifying the globally optimal or 1 ' if A- j.near-optimal channel estimate. We employ the RWBS t+, 1 -Cguided random search algorithm [11] to perform the Note that since (c*, s*), (-c*,-s*), (-jc*, +js*) andouter optimization task. The detailed RWBS algorithm (+1jc*,-js*) are all legitimate solutions of the joint203o................................................ Fig. depicts the bit error ratio (BER of the blind joint.......................................-SNR5dB ML optimization scheme using differential encoding,l o SNR-= 10dB in comparison to the BERs of the optimal maximum e10- .......................................likelihood sequence estim ator in the perfectly knowno channel scenario both with and without differentialllL:::::::SNR=20dB encoding. It is seen that the proposed blind scheme v.) ..i............................. ....................................only...induced.....by...less....than..1lydBdcedegradationId de rainio i NSN....terms...compared......to..the...perfectms omchannelhe erscenariol s en usingini 0-2- SNR=30dB differential encoding. We also investigated using anappropriately constituted GA to perform the upper-leveloptimization, and the results obtained by this GA-basedl 20300 4000 6000 blind joint ML estimation scheme are presented ino 2000 4000 6000 8000 10000 Figs. 4 and 5. By comparing Fig. 1 withFig. 4, it canbeNo. ofVA evaluations seen that both the RWBS and GA based schemes have asimilar convergence speed in terms ofthe total numberFig. 1. Mean square error against the number of VA evaluationsaveraged over 50 simulation runs using the RWBS algorithm for the ofVA evaluations required. It can also be seen that theSIMO channel listed in Table I. The length of the data samples is true estimation accuracy ofthe RWBS-based scheme isN = 50. better than that of the GA-based one, as confirmed bycomparing Fig. 2 and Fig. 5.100 IV. CONCLUSIONSA global optimization method referred to as the RWBS SNR- 5dB algorithm, has been developed for blind space-time11-2 equalization of SIMO systems based on joint MLSNR = 10dB channel estimation and data detection. The proposedSNR = 20dB algorithm provides the best performance in comparisonto a range of other blind adaptive schemes designed1o-4 - for SIMO systems, at the expense of a higher compu- 10 ~~~~~~SNIR= 30dB tational complexity. Our simulation study has shownl______l__l__l that this blind joint ML optimization scheme requires0 2000 4000 6000 8000 10000 a low number of received data samples to approachNo. ofVA evaluations themaximum likelihood sequence estimation scheme'sFig. 2. Mean tap error against the number of VA evaluationsaveraged over 50 simulation runs using the RWBS algorithm for othe SIMO channel listed in Table I. The length of the data samples 10 ....i.i.i.i.l.i i.i.i.l.i.i.i.iis N 50. known channel- K- known, differerential coding0 E j blind, differential codingML estimation problem (7), the channel estimate c may LU ..............converge to c, -c, jc or -jc. 1--0-Figs. l and 2 show the evolution of the MSE and Xo;,MTEaveragedover50simulationrunsandfordifferent °1-3EEEEEEEEEEEEE EEEEEEEEEvalues of signal to noise ratio (SNR), respectively, a) 000 00000000;;000000obtained by the proposed blind joint ML optimization m -4- : J'''' ''-scheme using the RWBS. It can be seen from Fig. 1, 10that the MSE converged to the noise floor. The phase fJ . 00ambiguity of 900, 1800 or 2700 associated with the ________________________blindML estimate of s cannot be resolved by the blind 0 2 4 6 8adaptive scheme itself. In practice, this ambiguity is SNR (dB)resoledeiher y adotingdiffeentia encding r by Fig. 3. Comparison ofbit error rate performance using the maximumemploying a few pilot training symbols. We assist the likelihood sequence detection for the SIMO channel listed in Table I.blind adaptive scheme by using differential encoding. The length of data samples for the blind scheme is N =50.204............................................weighted boosting search and (B - accuracy requiredSNR-=5dB - - for terminating the weighted boosting search.loo Outer loop: generations For g= 1: NGeneration initialization: Initialize the popu-20dB.lation by setting u( ) u est and randomly-1 SNR = 2OdB ~ ~ ~ ~ ~ ~~~~U U~S10' _ NR-20dB generating the rest ofthe population memberss, here denotes the........solution found in the previous generation. If-2 SNR = 30dB g 1 U is also randomly chosen..................................Weighted osting search in itializa-...................tion: ssign the initial distribution-31 weight i(0) , 1 < i < Ps,o l000 2000 3000 4000 5000 6000 7000 for the population, and calculate theNo. ofVA evaluations cost function value of each pointFig. 4. Mean square error against number of VA evaluations according toaveraged over 50 runs using the GA for the SIMO channel listed (g) 1 < < Psin Table I. The length of data samples N = 50. j-= Ui101 ...................Inner loop: eighted boosting.......sea rch or:::::::::::::::::Step 1: Boosting10° 1) Find0t ...... _ ,..0O : ;;E ~~~~~~~~~andQl -2 SR1d lberst arg mlx.:::::::::::: SNR = 30dB Denote an Slo-3~~~~~~~~~~~~~)~~~~(9) (9)............~i 30dB ot an] ut ~~~~~~~orst = 2worst0 1000 2000 3000 4000 5000 6000 70002)NraietecsfutonvlNo. ofVA evaluations uesFig. 5. Mean tap error against the number of VA evaluations pi= J 1 < it < Ps-averaged over 50 simulation runs using the GA for the SIMO channel Zm= I Jmlisted in Table I. The length of data samples is N =50.3)Cmueawihngfco 3according toperformance. PAPPENDIX. REPEATED WEIGHTED BOOSTING i=lSEARCH 4) Update the distribution weightingfor 1 <i <PsConsider solving the generic optimization problem-m*Ju,(2 ~(,t) f d(ti1)3/t, _ for /3t K< mun(u) (12 5() l (t_ )13tlJi, for /3t >1where Ui defines the feasible set of u, using the RWBSannomlzthm corigoalgorithm of [11]. The algorithm is detailed below.Specify the following algorithmic parameters: PS -6 (t) ZT_ (<) l< <population size, NG.. number of generations in therepeated search, NB - number of iterations in the Step 2. Parameter updating2051) Construct the (Ps + 1)th point REFERENCESusing the formula[1] J.J. Shynk and R.P. Gooch, "The constant modulus arrayPs for cochannel signal copy and direction finding," IEEE63 = (t)ug). Trans. Signal Processing, Vol.44, No.3, pp.652-660,i=1 1996.[2] J.J. Shynk, A.V. Keerthi and A. Mathur, "Steady-state2) Construct the (Ps + 2)th point analysis ofthe multistage constant modulus array,"IEEEusing the formula Trans. Signal Processing, Vol.44, No.4, pp.948-962,(g) (g) -1996.UPs+2 = Ubest+ (Ubest -UPS+)* [3] C.-Y Chi, C.-Y Chen, C.-H. Chen and C.-C. Feng,"Batch processing algorithms for blind equalization us-3) Compute the cost function val- ing higher-order statistics," IEE Signal Processing Let-ues J(up5+i) and J(up5+2) for ters, Vol.20, No.1, pp.25-49, 2003.these two points and find [4] S. Chen, A. Wolfgang and L. Hanzo, "Constant modulusalgorithm aided soft decision-directed blind space-timei* = arg Min J(ui). equalization for SIMO channels," inProc. VTC2004Falli=Ps+l,PS+2 (Los Angeles, USA), Sept.26-29, 2004, CD-ROM, 54) The pair (u* J(ui*)) then re- pages.(ug,(uworsth [5] H. Gazzah, P.A. Regalia and J.-P. Delmas, "Asymptoticplaces (uworst ) Jiworst) in the eigenvalue distribution of block Toeplitz matrices andpopulation. application to blind SIMO channel identification," IEEEIf IIUPS+l- UPS+2|| < (B, exit Trans. 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Harris, "Experimentssearch generation, which otherwise would be lost due withrepeating weighted boosting search for optimizationto random sampling based initialization. In the inner in signal processing applications," submitted to IEEEloop optimization, there is no need for every members Trans. Systems, Man and Cybernetics, Part B, 2004.of the population toconvergetoa(local)minimu [12] S. Chen and Y Wu, "Maximum likelihoodjoint channel of the population to converge to a (local) minimum, and data estimation using genetic algorithms," IEEEand it is sufficient to locate where the minimum lies. Trans. Signal Processing, Vol.46, No.5, pp.1469-1473,Thus, the number ofweighted boosting iterations, NB, 1998.can be set to a relatively small integer and the accuracyrequired for terminating the weighted boosting search,B,can be set to a relatively large value. This reducesthe search efficient, achieving convergence after a smallnumber of the cost function evaluations. It should benoted, although the formal proof is still required, thatinconjunction with a sufficiently high number of gen-erations, the algorithm will reach the globally optimalsolution.

A fast blind SIMO channel identification The solution found in the gth generation is algorithm for sparse sources,&quot;

algorithmic parameters have to be found empirically, [10] A. Dogandzic

Asymptotic places (uworst ) Jiworst) in the eigenvalue distribution of block Toeplitz matrices and population. application to blind SIMO channel identification,&quot;

Batch processing algorithms for blind equalization us3) Compute the cost function val- ing higher-order statistics,&quot;

Constant modulus algorithm aided soft decision-directed blind space-time i* = arg Min J(ui). equalization for SIMO channels,&quot;

identification of SIMO channels: a support vector reThis yields the solution U = Ubest gression approach,&quot; in

it retains the information obtained by the previous [11]

of the population toconvergetoa(local)minimu [12]

Steady-state 2) Construct the (Ps + 2)th point analysis ofthe multistage constant modulus array,&quot;IEEE using the formula Trans.

To guarantee a global optimal solution as well as to [8]

using the formula [1]

Blind joint maximum likelihood channel estimation and data detection for single-input multiple-output systems

Blind joint maximum likelihood channel estimation and data detection for single-input multiple-output systems

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