Artificial Bee Colony and Tabu Search Enhanced TTCM Assisted MMSE Multi-User Detectors for Rank Deficient SDMA-OFDM System

Abstract

In this paper, we propose two novel and computationally efficient metaheuristic algorithms based on Artificial Bee Colony (ABC) and Tabu Search (TS) principles for Multi User Detection (MUD) in Turbo Trellis Coded Modulation based Space Division Multiple Access Orthogonal Frequency Division Multiplexing system. Unlike gradient descent methods, both ABC and TS 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 algorithms are compared with each other and also against Genetic Algorithm (GA) and K-Best sperical decoding algorithm based MUD. Simulation results establish better performance, computational efficiency and convergence characteristics for ABC and TS 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 outperforms the traditional MMSE MUD.