Performance of Some Metaheuristic Algorithms for Multiuser Detection in TTCM-Assisted Rank-Deficient SDMA-OFDM System

Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2010, Article ID 473435, 11 pages doi:10.1155/2010/473435 Research Article Performance of Some Metaheuristic Algorithms for Multiuser Detection in TTCM-Assisted Rank-Deficient SDMA-OFDM System P. A. Haris, E. Gopinathan, and C. K. Ali Department of Electronics and Communication Engineering, National Institute of Technology, NIT Campus P.O., Calicut, Kerala 673601, India Correspondence should be addressed to P. A. Haris, harisabdul Received 1 June 2010; Revised 13 October 2010; Accepted 6 December 2010 Academic Editor: Sangarapillai Lambotharan Copyright © 2010 P. A. Haris et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We propose two novel and computationally efficient metaheuristic algorithms based on Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) principles for Multiuser Detection (MUD) in Turbo Trellis Coded modulation- (TTCM-) based Space Division Multiple Access (SDMA) Orthogonal Frequency Division Multiplexing (OFDM) system. Unlike gradient descent methods, both ABC and PSO methods ensure minimization of the objective function without the solution being trapped into local optima. These techniques are capable of achieving excellent performance in the so-called overloaded system, where the number of transmit antennas is higher than the number of receiver antennas, in which the known classic MUDs fail. The performance of the proposed algorithm is compared with each other and also against Genetic Algorithm- (GA-) based MUD. Simulation results establish better performance, computational efficiency, and convergence characteristics for ABC and PSO methods. It is seen that the proposed detectors achieve similar performance to that of well-known optimum Maximum Likelihood Detector (MLD) at a significantly lower computational complexity and outperform the traditional MMSE MUD. 1. Introduction Multiinput-Multioutput Orthogonal Frequency Division Multiplexing (MIMO-OFDM) [1] is considered as candidates for future 4G broadband wireless services. Among various topics related to MIMO-OFDM technologies, Space Division Multiple Access (SDMA) [2] based OFDM communication invoking Multiuser Detection (MUD) techniques has recently attracted intensive research interests. In SDMA MIMO systems the L different users transmitted signals are separated at the base-station (BS) using their unique, userspecific spatial signature, which is constituted by the Pelement vector of their channel transfer function between the user’s single transmit antenna and the P different receiver antenna elements at the BS, upon assuming flat fading channel conditions in each of the OFDM subcarriers. A variety of MUDs [3, 4] have been proposed for separating different users at the BS on a per-subcarrier basis. The most popular among them is constituted by the Minimum Mean Squared Error (MMSE) MUD and was found to give poor performance. ML detection gives the best performance having dramatically increased computational complexity. By incorporating Forward Error Correction (FEC) schemes such as Turbo Trellis Coded Modulationb (TTCM) [5], the achievable performance can be further improved. In the existing literature, although there are a number of papers dealing with optimization-based approaches for MIMO-MUD, metaheuristic approaches still remain largely unexplored. Metaheuristics are general high-level procedures that coordinate simple heuristics and rules to find good (often optimal) approximate solutions to computationally difficult combinatorial optimization problems [6]. In the context of SDMA multiuser MIMO OFDM systems, none of the known classic multi user detectors allow the number of transmitters (Nt ) to be higher than the number of receivers, which is often referred to as an overloaded scenario, owing to the constraint imposed by the rank of the MIMO channel matrix. Against this background, 2 EURASIP Journal on Wireless Communications and Networking User 1 TTCM encoder Interleaver IFFT MS1 User 2 TTCM encoder Interleaver IFFT MS2 . . . . . . . .. . .. . .. User L TTCM encoder Interleaver IFFT MSL User 1 TTCM decoder De-interleaver User 2 TTCM decoder De-interleaver .. . . .. User L TTCM decoder .. . .. . De-interleaver SDMA MIMO channel FFT MUD (ABC or PSO) FFT .. . P-element receiver antenna array FFT Figure 1: Schematic of TTCM-MMSE-ABC-MUD-SDMA-OFDM uplink system. in this paper we propose two computationally efficient metaheuristic algorithms based on ABC [7–11] and PSO [12–15] for multiuser detection in SDMA-OFDM systems, which provide an effective solution to the multiuser MIMO detection problem in the above-mentioned high-throughput rank-deficient scenario. Both ABC and PSO are efficient stochastic optimization tools with the capability of avoiding local minima, a feature not present in gradient search-based nonlinear optimization methods. The methods proposed approach the optimum performance of the ML detector. Finally, the computational complexity of the proposed schemes is significantly lower than that of the optimum ML system, especially in high-throughput scenarios. Our major contributions in this paper are (i) the development of two relatively accurate, computationally efficient metaheuristic algorithm suitable for multi user detection in SDMA-OFDM system; (ii) a thorough analysis of the performance of the proposed algorithms under both fully loaded and overloaded scenario; (iii) computational complexity comparison of the proposed algorithms with existing MUDs such as ML and MMSE. From the analysis it is found that the ABC- and PSO-based methods outperform the existing MMSE- and GA-based MUDs. The structure of this paper is as follows. Section 2 provides a description of the related works. The SDMA MIMO system model is described in Section 3, while the proposed MUDs based on ABC and PSO are explained in Section 4. Our simulation results are provided in Section 5, while the associated complexity issues are discussed in Section 6. Our final conclusions are summarized in Section 7. but they suffer from performance loss. The nonlinear MUDs such as SIC and PIC [16] are prone to error propagation. ML detector was found to give best performance at the cost of dramatically increased computational complexity. The performance of numerous known classic MUD techniques such as Vertical Bell Labs Layered Space-Time architecture (V-BLAST) [17] and the QR Decomposition combined with the M-algorithm (QRD-M) [18] will fail in the overloaded scenario where the number of users exceeds the number of receivers. Damen et al. [19] proposed a powerful sphere decoding (SD) algorithm which was suitable for overloaded MIMO MUD. The derivatives of SD such as Optimized Hierarchy Reduced Search Algorithm ( (...truncated)