VIRGINIA POLYTECHNIC INSTITUTE & STATE UNIVERSITY MOBILE & PORTABLE RADIO RESEARCH GROUP MPRG Combined Multiuser Detection and Channel Decoding with Receiver Diversity IEEE GLOBECOM Communications Theory Mini-Conference Sydney, Australia November 10, 1998 Matthew C. Valenti and Brian D. Woerner Mobile and Portable Radio Research Group Virginia Tech Blacksburg, Virginia

Outline Outline of Talk n Multiuser detection for TDMA systems. n Macrodiversity combining for TDMA. n Turbo-MUD for convolutionally coded asynchronous multiple-access systems. n Proposed System. n The Log-MAP algorithm. u For decoding convolutional codes. u For performing MUD. n Simulation results for fading channels.

MUD for TDMA Multiuser Detection for the TDMA Uplink n For CDMA systems: u Resolvable interference comes from within the same cell. u Each cochannel user has a distinct spreading code. u Large number of (weak) cochannel interferers. n For TDMA systems: u Cochannel interference comes from other cells. u Cochannel users do not have distinct spreading codes. u Small number of (strong) cochannel interferers. n MUD can still improve performance for TDMA. u Signals cannot be separated based on spreading codes. u Delay, phase, and signal power can be used.

Macrodiversity Macrodiversity Combining for the TDMA Uplink n In TDMA systems, the cochannel interference comes from adjacent cells. n Interferers to one BS are desired signals to another BS. n Performance could be improved if the base stations were allowed to share information. n If the outputs of the multiuser detectors are log-likelihood ratios, then adding the outputs improves performance. BS 1 BS 2 BS 3 MS 3 MS 1 MS 2

Macrodiversity Macrodiversity Combiner n Each of M base stations has a multiuser detector. u Each MUD produces a log-likelihood ratio of the code bits. u The LLR’s are added together prior to the final decision. Multiuser Estimator #1 Multiuser Estimator #M

Turbo MUD Turbo Multiuser Detection n Most TDMA systems use forward error correction (FEC) coding. n The process of multiuser detection and FEC can be combined using iterative processing. u “Turbo-MUD” n This is analogous to the decoding of serially concatenated turbo codes, where: u The “outer code” is the convolutional code. u The “inner code” is an MAI channel. F The MAI channel can be thought of as a time varying convolutional code with complex-valued coefficients.

Turbo MUD Turbo MUD: System Diagram Convolutional Encoder #K n(t) AWGN SISO MUD Bank of K SISO Decoders Estimated Data Turbo MUD interleaver #K multiuser deinterleaver multiuser interleaver MAI Channel APP Convolutional Encoder #1 interleaver #1 MUX “multiuser interleaver”

Turbo MUD w/ Macrodiversity Macrodiversity Combining for Coded TDMA Systems n Each base station has a multiuser estimator. n Sum the LLR outputs of each MUD. n Pass through a bank of Log-MAP channel decoder. n Feed back LLR outputs of the decoders. Multiuser Estimator #1 Multiuser Estimator #M Bank of K SISO Channel Decoders

Log-MAP Algorithm The Log-MAP Algorithm n The Viterbi Algorithm can be used to implement: u The MUD (Verdu, 1984). u The convolutional decoder. n However, the outputs are “hard”. n The iterative processor requires “soft” outputs. u In the form of a log-likelihood ratio (LLR). u The symbol-by-symbol MAP algorithm can be used. F Bahl, Cocke, Jelinek, Raviv, (BCJR Algorithm) u The Log-MAP algorithm is performed in the Log domain, F Robertson, Hoeher, Villebrun, F More stable, less complex than BCJR Algorithm. n We use Log-MAP for both MUD and FEC.

Log-MAP MUD n Received signal at base station m: n Where: u a is the signature waveform of all users. F Assumed to be a rectangular pulse. u  k,m is a random delay of user k at receiver m. u P k,m [i] is power at receiver m of user k’s i th bit. n Matched filter output for user k at base station m: MAI Channel Model

Log-MAP MUD Log-MAP MUD Algorithm: Setup n Place y and b into vectors: n Place the fading amplitudes into a vector: n Compute cross-correlation matrix for each BS: u Assuming rectangular pulse shaping.

Log-MAP MUD S0S0 S3S3 S2S2 S1S1 i = 0i = 6i = 3i = 2i = 1i = 4i = 5 Log-MAP MUD Algorithm: Execution Jacobian Logarithm: Branch Metric:

Simulation Simulation Parameters n The uplink of a TDMA system was simulated. u 120 degree sectorized antennas. u 3 cochannel interferers in the first tier. F K=3 users. F M=3 base stations. u Fully-interleaved Rayleigh flat-fading. u Perfect channel estimation assumed. u Each user is convolutionally encoded. F Constraint Length W = 3. F Rate r = 1/2. u Block size L=4,096 bits F 64 by 64 bit block interleaver

Simulation Performance for Constant C/I = 7dB

Simulation Performance for Constant Eb/No = 6dB

Conclusions Conclusion and Future Work n MUD can improve the performance of TDMA system. n Performance can be further improved by: u Combining the outputs of the base stations. u Performing iterative error correction and multiuser detection. n This requires that the output of both the MUD’s and FEC-decoders be in the form of log-likelihood ratios. u Log-MAP algorithm used for both MUD and FEC. n The study assumes perfect channel estimates. u The effect of channel estimation should be considered. u Decision directed estimation should be possible. F Output of each base station can assist estimation at the others.

Uncoded Performance for Constant C/I n C/I = 7 dB n Performance improves with MUD at one base station. n An additional performance improvement obtained by combining the outputs of the three base stations.

Uncoded Performance for Constant E b /N o n Performance as a function of C/I. u E b /N o = 20 dB. n For conventional receiver, performance is worse as C/I gets smaller. n Performance of single-base station MUD is invariant to C/I. u Near-far resistant. n For macrodiversity combining, performance improves as C/I gets smaller.