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Investigation on the evolutionary algorithms with their applications in MIMO detecting systems

机译:进化算法及其在MIMO检测系统中的应用研究

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摘要

In this paper, with the purpose of integrating the advantages of both the genetic algorithm and the particle swarm optimization, a new genetic particle swarm optimization (GPSO) algorithm is proposed. Furthermore, these three evolutionary algorithms are successfully applied to address the MIMO detection problem. Simulation results reveal that the GPSO-based detection algorithm takes much less population size and iteration number when compared with the particle swarm optimization-based detection method and the genetic algorithm-based detection method. Besides, when compared with the optimal maximum likelihood detection method, the GPSO-based detection algorithm can strike a much better balance between the BER performance and the computational complexity.
机译:为了兼顾遗传算法和粒子群优化算法的优点,提出了一种新的遗传粒子群优化算法。此外,这三种进化算法已成功应用于解决MIMO检测问题。仿真结果表明,与基于粒子群优化的检测方法和基于遗传算法的检测方法相比,基于GPSO的检测算法所需要的种群数量和迭代次数要少得多。此外,与最佳最大似然检测方法相比,基于GPSO的检测算法可以在BER性能和计算复杂度之间取得更好的平衡。

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