[IEEE 2005 2nd International Symposium on Wireless Communication Systems - Siena, Italy (05-09 Sept. 2005)] 2005 2nd International Symposium on Wireless Communication Systems - Interference Rejection and Equalization for DS/CDMA Multiuser Communication

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<ul><li><p>Interference Rejection and Equalization forDS/CDMA Multiuser Communication</p><p>Hsin-Chang ChenDepartment of Electronic EngineeringChung Yuan Christian University</p><p>Chung-Li, 32023 Taiwan</p><p>Li-Der JengDepartment of Electronic EngineeringChung Yuan Christian University</p><p>Chung-Li, 32023 Taiwan</p><p>Chung-Hsuan WangDepartment of Communication Engineering</p><p>National Chiao Tung UniversityHsinchu, 30056 Taiwan</p><p>Abstract- In direct-sequence code division multiple access(DS/CDMA) mobile communication systems with time-varyingmultipath channels, both intersymbol interference (ISI) andmultiple-access interference (MAI) must be considered. It hasbeen shown that iterative equalization and decoding method is anefficient technique to combat ISI. Based on this approach, we pro-pose a turbo equalization-like receiver for multiuser DS/CDMAsystems in multipath channels. The proposed MAI-rejection/ISIsuppression filter is based on a cost function evaluated at thePN code despreader's and data detector's output. Besides, ourscheme does not require a training signal. Simulation results showthat the proposed iterative receiver performs better than that ofnon-iterative scheme, and the BER decreases with the numberof iteration increases.</p><p>I. INTRODUCTION</p><p>In wireless environments, the multipath fading is animportant problem for signal detection. These different pathswith different attenuations cause the signal to fade both inamplitude and in time. The adaptive filter receivers can beused to compensate this phenomenon and able to adapt todifferent channel characteristics. Adaptive receivers for singleuser detection performs better than conventional receivers.They perform interference rejection, despreading, and in somecases act as the RAKE receivers which combine the multipathcomponents of the signal. There have been considerableworks done in the past few years on adaptive multiuserdetection in [4] and [5] based on the minimization of meansquare error (MMSE) between the finite impulse response(FIR) filter output and the data.</p><p>A low complexity soft multiuser detector is proposed in[6], which uses the same decision statistic as the conventionalRAKE receiver and performs soft-interference cancellationand instantaneous MMSE filtering. MMSE linear filters forcontinuous transmission are presented in [13] which take intoaccount both ISI and MAI. To get the better performance, thedecision feedback equalizer (DFE) can be used for iterativeinterference cancellation. The structure of the receiver withthe chip-level DFE has been investigated. On the otherhand, Yihand [12] presented a design of iterative multiuserdetection for bit-interleaved coded modulation (BICM) signalsin CDMA systems over multipath Rayleigh fading channels.</p><p>In digital communication systems, error-correction</p><p>codes can improve the system performance. Furthermore,conventional solutions consider both equalization and channelcoding which are done separately. Recently, a novel techniqueconsidering the equalization and decoding jointly, is called"turbo equalization". Combining turbo coding and multiuserdetection in CDMA has shown to get the system performanceclose to single user bound with a few iterations of jointdetection and decoding.</p><p>It has been shown that iterative equalization and decodingmethod has proved a clever technique for combating ISI [3].Based on this approach, we propose a turbo equalization-likereceiver for multiuser DS/CDMA systems in multipathchannels. The proposed MAI-rejection/ISI suppression filteris based on a cost function evaluated at the PN codedespreader's and data detector's output. Besides, our schemedoes not require a training signal.</p><p>The rest of this paper is organized as follows. Section IIgives a general description of the transmitter and receiverscheme of our proposed system. In this section we also definetwo cost functions in the iterative process. Section III presentsthe log-likelihood ratio transmission for the new cost functionto update filter tap-weights. Numerical results and discussionare given in section IV. Finally, section V summarizes ourmajor results.</p><p>TI. SYSTEM MODELThe block diagram of the proposed scheme is shown in</p><p>Fig. 1, where the number of active users is K. A binarydata sequence of each user is encoded by a convolutionalencoder and then fed into an interleaver. After interleaving, thecoded data sequence is spreaded by the user-specific signaturesequence of length A1. Finally, the transmitted data sequenceis modulated by a standard binary phase-shift keying (BPSK)modulator. In this paper, we assume a coherent receiver suchthat the multipath channel can be modeled as a finite impulseresponse (FIR) filter with impulse response [ho, hl,..., hp],and the transmitted signal is corrupted by thermal noise andMAI as well. The thermal noise in the channel is the so-called additive white Gaussian noise (AWGN) with the powerspectral density (PSD) No. The received sample sequence isfiltered and decoded by a adaptive filter and convolutional</p><p>0-7803-9206-X/05/$20.00 2005 IEEE 284</p></li><li><p>decoder respectively. In order to make better performance of asour system, iterative algorithm is employed in the proposedreceiver.</p><p>user data #1</p><p>p</p><p>Zq,m (n) = , hps',m_p (n)p=o</p><p>K P</p><p>+ , S hpsj mn-p (n) + Nq,m (n)j=2,jok p=O</p><p>'Zq(n) L</p><p>algorivh spreadingcodes LOE (bq(n))I iter hinterleaver</p><p>!iterative scheme</p><p>Fig. 1. Block diagram of the proposed system.</p><p>A. TransmitterFor the proposed multiuser DS/CDMA communication sys-</p><p>tems, assume the k-th user is the desired user. The desireduser transmit a sequence of binary information, whose el-ements take on values of 0 or 1 equiprobably, then thedata vector for n-th block can be expressed as bk (n) =[bk (n) , bk (n), bk (n)], k = , ,K, where k denotesthe k-th user, Q is the number of elements in a block. Thisdata is first encoded by a recursive systematic convolutionalcode with rate 1/2 shown in Fig. 2. The generator matrixused in this paper isG [i,1+D+D2. The encoded dataXk,q (n), 1, 2, is the codeword of the k-th user, n-th block,q-th bit. Then the encoded data is feed into the interleaver withsize Q. In this paper, we consider both the block interleaverand S-random interleaver. After interleaving, the coded datasequence is spreaded by the user-specific signature sequence oflength Al. The transmitted data sequence eventually is modu-lated by a standard BPSK modulator, i.e., +1,0 -1}.Finally, these data sequences are combined by a multiplexerand transmit over the multipath channel.</p><p>where si,m (n),j = 1,2, , K is the binary input signals{+1, -1} with equal probabilities; the second term is the MAIsequence; Nq,m (n) is the AWGN sequence with variance o2.Considering the proposed MAI/ISI rejection scheme illustratedin Fig. 1, we define the number of taps of the transversal filterbe t and the tap-weight vector as</p><p>Wq (n) = [Wq,l (n) Wq,2 (n) Wq,t (n)]TAssuming</p><p>Zq (n) = [ZAl(q-1)+l (n) ZM(q-1)+2 (n) ZA(q-1)+,N (n)]Tand</p><p>ZA(q-1) (n) ZAI(q-1)+AI-1 (n)ZM&gt;(q-1)-1 (n) ZAI(q-l)+AI-2 (n)</p><p>Uq (n) = . (2)</p><p>LZAI1(q-1)-(t-1) (n) ... ZAI(q-l)+A,I-t (n)i</p><p>the estimated interference (MAI+ISI) can be obtained in thefollowing</p><p>Iq (n) = [IM(q-1)+1 (n) iM(q-1)+2 (n) *A.*Z(q-1)+M (nt)]</p><p>= U (n) Wq (n). (3)</p><p>Let C = [Cl C2 CM 1T be the signature sequence of thedesired user, we have CTC = M. For iterative interferencerejection and decoding, our multiuser receiver utilizes theadaptive filter output and the a priori information of desireduser data to get the soft estimates. In the first iteration, no apriori probability is available, so Pr {dE (n) = 1} = 1/2 isused for soft outputs. It is desired that the adaptive filter outputgives a perfect MAIMISI estimation so that the normalizeddespreader output</p><p>dq (n) = (n)-Iq (n)TC/M (4)behaves like a desired user's output. In other words, we hopethat signal at the despreader output has the same statistic asan ideal BPSK signal. To achieve the goal we formulate thecost function</p><p>Fig. 2. Recursive systematic convolutional encoder.</p><p>B. ReceiverThe received sample sequence Zq,m (n) of the desired user</p><p>in n-th block, q-th bit, and m-th chip can be thus expressed</p><p>Jq (n) = E{ d, (n)-dq (n) }</p><p>where</p><p>dE (n) = {+1 dq (n) &gt; 0-1, otherwise</p><p>(5)</p><p>(6)</p><p>is the hard decision for the despreader output using for thepseudo training signal. Employing Least Mean Square (LMS)</p><p>285</p><p>(1)</p></li><li><p>algorithm to minimize the cost function Jq (n) for updatingthe set of weight every bit interval. we need to evaluate</p><p>Wq+j (n) =Wq (n) - uaJ(t (n)o9Jq (n)</p><p>-W, (n)-HL [M (d (n) -dq (n)) Uq (n) C] (7)where At is the step size.</p><p>During the signal detection, the bit reliabilities must becomputed as a priori information of the soft-input soft-output(SISO) channel decoder. To get the bit reliabilities, the statis-tics</p><p>={[b, (n)-dq (n)] lb, (n) =+1} P {b,(n) = +l</p><p>+ {[bq(n)-dq (n)] Ibq(n)-l1} P{bq(n)=-1}. (12)</p><p>Similarly, using the LMS algorithm to minimize the costfunction Jq (n), we have</p><p>aJq' (n)&amp;Wq (n)</p><p>={ [(bq (n) dq(n))Uq(nI bq(n)+1} P{bq(n)=+1}</p><p>p (n) E {dq (n)}and</p><p>(n)-=Et dq(n) }-E{dq(n)}</p><p>of the set of bits dq (n) , q = 1, 2, * , Q, are required. Let</p><p>d (n) = di (n) d2 (n) ..dQ (n)the bit reliability is given as</p><p>{ q(n)Idd(n) =+1} 2LA(dq (n)) = ln = } = 2dq (n) (10)</p><p>P {dq (n) jd~E (n)--1 dAfter MAP decoding algorithm [2], the a posterior informa-tion can be obtained from the channel decoder in the following</p><p>Lo bq (n) Idq (n))</p><p>= Lc- dq (n) + LA (dq (n)) + LOE (dq (n) Ibq (n)) (11)</p><p>and hard decision will be made for bit estimates bq (n)of the desired user. Later, the bit estimates take the placeof the hard decision for the despreader output using foranother pseudo training signal. Consequently, we can see thatthe a-posteriori LLR, i.e., soft output, Lo (bq (n) Idq (n))calculated with the aid of the MAP algorithm can beviewed as comprising three additive soft-metric terms:LA (dq (n)), Lc 4q (n) , LOE (dq (n) Ibq (n)). The extrinsicinformation LOE (dq (n) Ibq (n)) which contains the softinformation of the current bit is re-interleaved and fed backinto the adaptive filter to update the a prior informationto get better soft information. The iteration process, whichrefines the soft output Lo (bq (n) iq (n)), q = 1 2, ... , Q,continues until the result converges. At the last iteration, thedecoder makes a hard decision for the user's data bits.</p><p>III. LOG-LIKELIHOOD RATIOAs mentioned above, we calculate the bit reliabilities of</p><p>filter's output dq (n) and expressed as LA (dq (n)). The re-liabilities are used as the a priori information in the SISOMAP decoder. The a posteriori information of the decoded</p><p>Zq(n) dq(n) - -&gt; LA(dq(n))Equa1izer Decde,i'~~~~~~~ A</p><p>(^</p><p>-))</p><p>-Lo (dq (n) bq (n))'4bq(n)1*-((f)(lJ</p><p>Fig. 3. Log-Likelihood Ratio transmission diagram.</p><p>bit bq (n) is expressed as Lo (bq (n) ldq (n)) which includesa priori information LA (dq (n)), extrinsic information andLOE (dq (n) lbq (n) ). After first iteration, the extrinsic infor-mation will be transformed into the a priori information for thefilter tap-weights updating. The LLR of the data bit bq (n) isdenoted as Lu bq (n)) and is defined to be the log of the ratioof the probabilities of the bit taking its two possible values,i.e.,</p><p>Lu (bq (1)) =ln (P (q (1) +)Pr (bq (n) = -1))</p><p>(15)</p><p>Given the LLR value Lu (bq (n)), it is possible to calculateFurthermore, when the first iteration is finished, we use the the probability that bq (n) = +1 or bq (n) =-1 as follows.</p><p>following cost function for more iterations We know that</p><p>Jq (n) = E {[bq (n)-dq (n)] } Pr (bq (n) = -1) = 1-Pr (bq (n) = +1)</p><p>286</p><p>(8)and</p><p>(9+1 (n) = ) (n)-p i 4 (n)</p><p>(13)</p><p>(14)</p><p>(16)</p></li><li><p>and take the exponent of both sides in Equation (15), we have</p><p>r(bq</p><p>Pr (bq (n) = +1)exp 1Lu (bq (n))} 1- Pr (bq (n) = +1) (17)</p><p>and</p><p>exp {Lu 1q (n)) ErPr bq (ni) = *1) (18)</p><p>m</p><p>Similarly,</p><p>7-1- {L I{ \ 11</p><p>Vr bq rn) = -1) = ^eL )1+exp{ Lu(bq(n))}t</p><p>Once the decoding process is finished, the soft informa-tion LOE (dq (n) lbq (n)) can be fed into the filter as apriori information. With the help of Pr (bq (n) = +) and</p><p>Pr (bq (n) = -1) from LOE (dq (n) 1bq (n)), we can utilizeit in Equation (12) and Equation (13) for tap-weights updating.Then, we have accomplished one iteration for our proposedsystem.</p><p>IV. NUMERICAL RESULTSWe present BER performance results for the proposed</p><p>receiver by simulations. The system parameters for simulationare described as follows. The spreading code used in thispaper is the orthogonal Walsh code of length 16. Thefilter length is 16 taps. The transmission channel responsefor simulation is [0.65, -0.52,0.39, 0.33, -0.19,0.06]. Inaddition, we consider two R = 1/2 convolutional codeswhose generator matrices are [1, 1++D2] (degree = 2), and[i 1+D+2+D3+D42 (degree = 4). Two kinds of interleaverare used: S-random interleaver and block interleaver of size16384.</p><p>Shown in Fig. 4 is the performance comparison betweenthe different number of iteration, where the active usernumber is 16 for convolutional codes of degree 2. S-randominterleaver is used for interleaving. Simulation results showthat the more the number of iteration, the performance isbetter. Fig. 5 shows the performance comparison betweenthe different number of active user for non-iteration withconvolutional codes of degree 2. Also, the S-randominterleaver is used in this case. The simulation results showthat the more the number of active users, the performance isworse. Fig. 6 shows the performance comparison betweenthe conventional scheme and the proposed scheme. Theactive user number equals 16, and S-random interleaveris used. Convolutional codes of degree 2 and 4 are alsoconsidered. Simulation results indicate that performance ofthe proposed system is better than that of the conventionalsystem. Fig. 7 shows the performance difference between</p><p>two cost functions Jq (n) = E { dq (n) - dq (n) } and</p><p>10</p><p>'.5 8.5Eb/No(dB)</p><p>9.5 10</p><p>Fig. 4. Performance comparison between different number of iterations: 16active user, convolutional codes degree = 2, S-random interleaver.</p><p>Non-iterative with Convolutional Codes degree = 2 and S-random interleaver</p><p>10-2</p><p>a)(a</p><p>m</p><p>5 8 9 10Eb/No(dB)</p><p>Fig. 5. Performance comparison between different number of active users:non-iteration, convolutional codes degree = 2, S-random interleaver.</p><p>Jq (n) = E {[bq (n)-dq (n)]} We find that in thenon-iterative case, the performance of using cost functionJq (n) is identical to that using cost function J' (n). But inthe iterative case, the performance of using cost functionJ' (n) is better than that using cost function Jq (n).</p><p>Fig. 8 illustrates the performance comparison between blockinterleaver and S-random interleaver. Simulation results showthat S-random interleaver is better than block interleaver.</p><p>V. CONCLUSIONS</p><p>In this paper, we have presented an efficient interferencecancellation scheme which combines a filter and an iterative</p><p>287</p><p>............................................ . ................DNon-iteration... ..</p><p>v.. E) 2n3d iteration</p></li><li><p>1Comparison between conventional scheme and proposed scheme</p><p>5 6 7 8 9Eb/No(dB)</p><p>cc</p><p>10</p><p>m -4Fn 10</p><p>1-0 11 12</p><p>C=omparison of different interleaver</p><p>....:..block interleaver</p><p>. t3 ~~~~~~S-r...</p></li></ul>


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