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二进制猫群算法在大规模MIMO天线选择中的应用
李淑梅,庄铭杰,庄弘
0
(华侨大学 工学院,福建 泉州 362021;福建省物联网科学研究院,福建 泉州 362000)
摘要:
在多输入多输出(MIMO)系统中,天线选择技术平衡了系统的性能和硬件开销,但大规模MIMO系统收发端天线选择复杂度问题一直没有得到很好的解决。基于信道容量最大化的准则,采用两个二进制编码字符串分别表示发射端和接收端天线被选择的状态,提出将二进制猫群算法(BCSO)应用于多天线选择中,以MIMO系统信道容量公式作为猫群的适应度函数,将收发端天线选择问题转化为猫群的位置寻优过程。建立了基于BCSO的天线选择模型,给出了算法的实现步骤。仿真结果表明所提算法较之于基于矩阵简化的方法、粒子优化算法具有更好的收敛性和较低的计算复杂度,选择后的系统信道容量接近于最优算法,非常适用于联合收发端天线选择的大规模MIMO 系统中。
关键词:  多输入多输出  天线选择  二进制猫群算法  信道容量  粒子群优化算法
DOI:
基金项目:福建省自然基金计划项目(2012J01270);泉州市科技计划重点项目(2012Z82);华侨大学自然科学基金(11BS430);2015级华侨大学研究生科研创新能力培养项目(1511322002)
Application of binary cat swarm optimization in large-scale MIMO antenna selection
LI Shumei,ZHUANG Mingjie,ZHUANG Hong
()
Abstract:
The antenna selection technique balances the performance and hardware cost in the multiple input multiple output(MIMO) systems,but the complexity issue of the large-scale transmit and receive antenna selection on MIMO system has not been fully solved. According to the criterion of maximizing channel capacity,in this paper,two binary coding strings are used to represent the selected states of the transmitter and receiver antennas respectively,and binary cat swarm optimization(BCSO) is applied to multiple antenna selection. The formula of MIMO channel capacity is used as the fitness value of the cats,then the problem of transmit and receive antenna selection is transformed into the location optimization of the cats.The antenna selection model with BCSO is established and and its implementation steps are given. The simulation results show that,the proposed algorithm has better convergence and lower computational complexity than the method based on matrix simplification and particle swarm optimization(PSO) algorithm,moreover it can get closer to the optimal algorithm in the aspect of channel capacity. The proposed algorithm is very suitable for the MIMO system with a large number of antennas in joint transmit and receive antenna selection.
Key words:  multiple input multiple output(MIMO)  antenna selection  binary cat swarm optimization  channel capacity  particle swarm optimization algorithm