Iterative Multi-User Detection for OFDM Using Biased Mutation Assisted Genetic Algorithms

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 Like-lihood (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/No 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.