A novel low complexity multiuser detector based on modi ed genetic algorithm in direct sequence-code division multiple access communication systems

In this paper, we present an ecient evolutionary algorithm for Multiuser Detection (MUD) problem in Direct Sequence-Code Division Multiple Access (DS-CDMA) communication systems. The optimum detector for MUD is the Maximum Likelihood (ML) detector, but its complexity is very high, and involves an exhaustive search to reach the best tness of the transmitted and received data. Thus, there has been much interest in suboptimal multiuser detectors with less complexity and reasonable performance. The proposed algorithm is a modied Genetic Algorithm (GA) which reduces the dimension of the search space, and provides a suitable framework for future extension to other optimization problems, especially for high dimensional ones. This algorithm is compared with ML and two famous model-free optimization methods: Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms, which have been used for MUD in DS-CDMA. The simulation results show that the performance of this algorithm is close to the optimal detector; it has very low complexity, and it works better in comparison to other algorithms.