Iterative Multi-User Detection for OFDM using Biased Mutation Assited Genetic Algorithms

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Description/Abstract

Space Division Multiple Access (SDMA) aided Orthogonal Frequency Division Multiplexing (OFDM) systems assisted by efficient Multi-User Detection (MUD) techniques have recently attracted intensive research interests. As expected, Maximum Likelihood (ML) detection was found to attain the best performance, although this was achieved at the cost of a high computational complexity. Forward Error Correction (FEC) schemes such as Turbo Trellis Coded Modulation (TTCM) can be efficiently amalgamated with SDMA-OFDM systems for the sake of improving the achievable performance without bandwidth expansion. In this contribution, a MMSE-aided Iterative GA (IGA) MUD is proposed for employment in a TTCM-assisted SDMA-OFDM system, which is capable of achieving a similar performance to that attained by its optimum ML-aided counterpart at a significantly lower complexity, especially at high user loads. Moreover, when the proposed novel Biased Q-function Based Mutation (BQM) scheme is employed, the IGA-aided system’s performance can be further improved by achieving an Eb/N0 gain of about 6dB in comparison to the TTCM-aided MMSE-SDMA-OFDM benchmarker system both in low- and high-throughput modem scenarios, respectively, while still maintaining a modest complexity.