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Based on the new discrete particle swarm op-timization(NDPSO) and the mutation operation of the genetic algorithm,a hybrid discrete particle swarm op-timization(HDPSO) algorithm is proposed and applied to the signal detection of the Vertical Bell-Labs Layered Space-Time(V-BLAST)system.Then two detection ap-proaches that employ the HDPSO and parallel interfer-ence cancellation(PIC) algorithms in V-BLAST system are proposed.One of approaches is that the HDPSO algorithm is used as the first stage of the PIC to provide a good initial point for successive stages of the PIC,the other is that PIC is embedded into the HDPSO al-gorithm to improve further the fi tness of the population at each generation.Such a hybridization of the HDPSO with the PIC can speed up its convergence.In addition,a better initial data estimate supplied by the HDPSO algorithm improves the performance of the PIC,and the embedded PIC improves the performance of the HDP-SO.Analysis and simulation results show that the pro-posed detection approaches have lower computational complexity than the optimal ML detection approach and better detection performance than other conventional detection approaches.
Based on the new discrete particle swarm op-timization (NDPSO) and the mutation operation of the genetic algorithm, a hybrid discrete particle swarm op-timization (HDPSO) algorithm is proposed and applied to the signal detection of the Vertical Bell-Labs Layered Space -Time (V-BLAST) system. Two detection ap-proaches that employ the HDPSO and parallel interfer-ence cancellation (PIC) algorithms in V-BLAST system are proposed. One of the approaches is that the the HDPSO algorithm is used as the first stage of the PIC to provide a good initial point for successive stages of the PIC, the other is that PIC is embedded into the HDPSO al-gorithm to improve further the fi tness of the population at each generation. Such a hybridization of the HDPSO with the PIC can speed up its convergence. In addition, a better initial data estimate supplied by the HDPSO algorithm improves the performance of the PIC, and the embedded PIC improves the performance of the HDP-SO. Analysis and simulation results show that the pro -posed detection approaches have lower computational complexity than the optimal ML detection approach and better detection performance than other conventional detection approaches.