A Genetic Algorithm-Assisted Semi-Adaptive MMSE Multi-User Detection for MC-CDMA Mobile Communication Systems

In this work, a novel minimum-mean squared-error (MMSE) multi-user detector is proposed for MC-CDMA transmission systems working over mobile radio channels characterized by time-varying multipath fading. The proposed MUD algorithm is based on a genetic algorithm (GA)-assisted per-carrier MMSE criterion. The GA block works in two successive steps: a training-aided step aimed at computing the optimal receiver weights using a very short training sequence, and a decision-directed step aimed at dynamically updating the weights vector during a channel coherence period. Numerical results evidenced BER performances almost coincident with ones yielded by ideal MMSE-MUD based on the perfect knowledge of channel impulse response. The proposed GA-assisted MMSE-MUD outperforms state-of-the-art adaptive MMSE receivers based on deterministic gradient algorithms, especially for high number of users