Weighting particle swarm, simulation annealing and local search optimization for S/MIMO MC-CDMA systems

This paper analyzes the complexity-performance trade-off of three heuristic approaches applied to synchronous multicarrier multiuser detection (MUD) of single/multiple transmit antennas and multiple receive antennas code division multiple access (S/MIMO MC-CDMA) systems. Weighting particle swarm optimization (WOPSO) and unitary Hamming distance search-based strategies, specifically 1-opt local search (1-LS) and simulation annealing (SA) multiuser detection algorithms, were analyzed in details using a single-objective antenna-diversity-aided optimization approach. Monte-Carlo simulations show that, after convergence, the performances reached by the three heuristic MUD (HEUR-MUD) S/MIMO MC-CDMA algorithms are identical, with computational complexities remarkably smaller than the optimum multiuser detector (OMUD). However, the computational complexities could differ substantially depending on the operation system conditions. The complexities of the HEUR-MUDs were carefully analyzed in order to demonstrate that 1-LS scheme provides the best trade-off between implementation complexity aspects and bit error rate (BER) performance when applied to multiuser detection of S/MIMO MC-CDMA systems with low order modulation.

[1]  Ying Zhao,et al.  Particle swarm optimization algorithm in signal detection and blind extraction , 2004, 7th International Symposium on Parallel Architectures, Algorithms and Networks, 2004. Proceedings..

[2]  Amir K. Khandani,et al.  A Near-Maximum-Likelihood Decoding Algorithm for MIMO Systems Based on Semi-Definite Programming , 2007, IEEE Transactions on Information Theory.

[3]  Hang Long,et al.  WLC06-6: PSO Selection of Surviving Nodes in QRM Detection for MIMO Systems , 2006, IEEE Globecom 2006.

[4]  S. Dreyfus,et al.  Thermodynamical Approach to the Traveling Salesman Problem : An Efficient Simulation Algorithm , 2004 .

[5]  F. Ciriaco,et al.  Particle Swarm and Quantum Particle Swarm Optimization Applied to DS/CDMA Multiuser Detection in Flat Rayleigh Channels , 2006, 2006 IEEE Ninth International Symposium on Spread Spectrum Techniques and Applications.

[6]  P.J.E. Jeszensky,et al.  Simplified Local Search Algorithm for Multiuser Detection in Multipath Rayleigh Channels , 2007, 2007 16th IST Mobile and Wireless Communications Summit.

[7]  Taufik Abrão,et al.  Local search multiuser detection , 2009 .

[8]  Yeheskel Bar-Ness,et al.  Partial sampling MMSE interference suppression in asynchronous multicarrier CDMA system , 2001, IEEE J. Sel. Areas Commun..

[9]  B.A. Angelico,et al.  Weighting Particle Swarm Optimization SIMO MC-CDMA Multiuser Detectors , 2008, 2008 IEEE 10th International Symposium on Spread Spectrum Techniques and Applications.

[10]  Lajos Hanzo,et al.  Antenna-diversity-assisted genetic-algorithm-based multiuser detection schemes for synchronous CDMA systems , 2003, IEEE Trans. Commun..

[11]  Muhammad Naeem,et al.  An Application of GA for Symbol Detection in MIMO Communication Systems , 2007, Third International Conference on Natural Computation (ICNC 2007).

[12]  S.I. Shah,et al.  Heuristics Assisted Detection in High Speed Wireless Communication Systems , 2006, 2006 IEEE International Multitopic Conference.

[13]  B. Sundar Rajan,et al.  A Low-Complexity Detector for Large MIMO Systems and Multicarrier CDMA Systems , 2008, IEEE Journal on Selected Areas in Communications.

[14]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[15]  Hua Wei,et al.  Reduced-complexity near-optimum genetic algorithm assisted multiuser detection for synchronous multicarrier CDMA , 2004, 2004 IEEE 59th Vehicular Technology Conference. VTC 2004-Spring (IEEE Cat. No.04CH37514).

[16]  Lajos Hanzo,et al.  Multiuser MIMO-OFDM for Next-Generation Wireless Systems , 2007, Proceedings of the IEEE.

[17]  Bernard Fino,et al.  Multiuser detection: , 1999, Ann. des Télécommunications.

[18]  Min Zhang,et al.  Sub-optimal Multiuser Detector Using a Time-varying Gain Chaotic Simulated Annealing Neural Network , 2007, Third International Conference on Natural Computation (ICNC 2007).

[19]  V. Cerný.  Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[20]  Ramjee Prasad,et al.  An overview of multi-carrier CDMA , 1996, Proceedings of ISSSTA'95 International Symposium on Spread Spectrum Techniques and Applications.

[21]  J. K. Lenstra,et al.  Local Search in Combinatorial Optimisation. , 1997 .

[22]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .