Genetic Local Search for Optimum Multiuser Detection Problem in DS-CDMA Systems
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The problem of multiple access interference (MAI) and intersymbol interference (ISI) suppression in code division multiple access (CDMA) systems is considered. By combining the theory of multiuser detection (MUD) and evolutionary computation, a hybrid genetic engine is proposed, suitable for the detection of CDMA signals in the presence of MAI and ISI. The proposed hybrid detector structure can be extended to most multiuser detectors and used as the base detector within the structure. Using random selection, mutation and crossover operators and a unique chromosome structure, the genetic algorithm evolves the base detector to a group of more efficient detectors in terms of bit-error rate performance. First, a new packet-level genetic MUD technique, using a conventional single user detector as the base detector, is proposed for asynchronous CDMA (ACDMA) with negligible ISI. Then the signal-subspace-based minimum mean square error detector is chosen as a base detector and wrapped inside the hybrid genetic engine to evolve to a better structure nearly to eliminate both ISI and MAI. The novelty of the proposed structure is the way the deterministic closed-form solution of the base detector is mapped to a genetic engine resulting in a group of more efficient and adaptive detectors