A review of heuristic near-optimum MuD for MC-CDMA systems

This paper reviews the performances of three heuristic near-optimum Multiuser Detection (MuD) approaches applied to the synchronous Multicarrier Code Division Multiple Access (MC-CDMA) communication systems. Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant-Colony Optimization (ACO) heuristic MuD detection algorithms are analyzed in details. The simulations show that, the bit error rate (BER) performance reached by all near optimum Heuristic MuD depends on the system operation conditions and the channel used. It was shown also that ACO MuD is capable of reducing significantly the computational complexity in comparison to that of Verdu's optimum MuD.