Performance analysis of particle swarm optimization and genetic algorithm in MIMO systems

Nidhi Sindhwani, M. Bhamrah, A. Garg, Dheeraj Kumar
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引用次数: 5

Abstract

The Multi-input multi-output (MIMO) technique is very useful for increasing the transmission reliability, high date rate, robustness and channel capacity in the modern radio communication systems. As many algorithms inspired from the nature have been proposed to evaluate the performance of MIMO system parameters like channel capacity, bit error rate and the best fitness value of channel capacity. But this technique delivers some issues such as increases the cost and complexity of hardware due to increasing number of antennas. To solve this issue, an effective optimal transmit antenna subset selection method is described in this paper with the aid of PSO. For each position of the particle, corresponding fitness function is evaluated and from there global best fitness values are chosen. The implementation of proposed work is done in the working platform MATLAB and the performance of MIMO are evaluated with various selection of transmit antenna subsets. Moreover, Improvement in Channel Capacity and BER with increasing SNR are discussed Hence, with the aid of proposed work, the selection of transmit antenna with the maximum capacity and minimum BER are made and which leads to the reduced hardware complexity and decrease number of antennas in the MIMO system.
粒子群优化和遗传算法在MIMO系统中的性能分析
多输入多输出(MIMO)技术对于提高现代无线电通信系统的传输可靠性、高数据率、鲁棒性和信道容量具有重要意义。由于受到自然界的启发,人们提出了许多算法来评估MIMO系统的性能参数,如信道容量、误码率和信道容量的最佳适应度值。但是这种技术带来了一些问题,比如由于天线数量的增加而增加了硬件的成本和复杂性。为了解决这一问题,本文利用粒子群算法提出了一种有效的最优发射天线子集选择方法。对于粒子的每个位置,计算相应的适应度函数,并从中选择全局最佳适应度值。在MATLAB工作平台上对所提出的工作进行了实现,并通过选择不同的发射天线子集对MIMO的性能进行了评估。此外,本文还讨论了信道容量和误码率随信噪比的提高而提高的问题。在此基础上,提出了选择最大容量和最小误码率的发射天线,从而降低了MIMO系统的硬件复杂度,减少了天线数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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