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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.

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