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大规模MIMO系统的智能天线选择与功率分配方法研究
引用本文:高洪元,苏雨萌,张世铂.大规模MIMO系统的智能天线选择与功率分配方法研究[J].系统工程与电子技术,2020,42(12):2669-2675.
作者姓名:高洪元  苏雨萌  张世铂
作者单位:哈尔滨工程大学信息与通信工程学院, 黑龙江 哈尔滨 150001
基金项目:国家自然科学基金(61571149);中国博士后基金特别资助项目(2015T80325);黑龙江省博士后科研启动金(LBH-Q19098);中央高校基本科研业务费专项资金-博士研究生科研创新基金(3072020GIP0812)
摘    要:为了满足大规模多输入多输出(multiple input multiple output, MIMO)系统的数据传输需求并降低系统能耗,提出一种基于量子化学反应优化的智能天线选择与功率分配方法。根据大规模MIMO系统不同时段的用户传输需求建立智能天线选择与功率分配模型,推导出其最大能效方程。为有效求解该非线性、多约束的混合优化难题,结合量子计算和化学反应优化机制的优势设计了量子化学反应优化算法,可得到最佳的天线选择与功率分配方案。仿真结果表明,所提的智能天线选择与功率分配方法能实时满足用户的信息传输需求,显著提高系统能效。针对不同的仿真场景,所提方法与现有的智能算法与分配策略相比均可得到最高的系统能效。

关 键 词:大规模多输入多输出  天线选择  功率分配  量子化学反应优化  
收稿时间:2020-05-12

Intelligent antenna selection and power allocation method for massive MIMO systems
Hongyuan GAO,Yumeng SU,Shibo ZHANG.Intelligent antenna selection and power allocation method for massive MIMO systems[J].System Engineering and Electronics,2020,42(12):2669-2675.
Authors:Hongyuan GAO  Yumeng SU  Shibo ZHANG
Institution:College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Abstract:In order to meet the data transmission requirements of massive multiple input multiple output (MIMO) systems and reduce the energy consumption of system, an intelligent antenna selection and power allocation method based on quantum chemical reaction optimization is proposed. According to the users' transmission requirements in different periods of massive MIMO systems, the intelligent antenna selection and power allocation model is established, then the maximum energy efficiency equation is derived. To effectively tackle the nonlinear, multi-constrained hybrid optimization problem, a quantum chemical reaction optimization algorithm is designed to obtain the optimal antenna selection and power allocation scheme by combining the advantages of quantum computation and chemical reaction optimization mechanism. Simulation results show that the proposed intelligent antenna selection and power distribution method can meet the transmission requirements of users in real time and significantly improve the system energy efficiency. Compared with existing intelligent algorithms and allocation strategies, the proposed method can achieve the highest system energy efficiency in different simu-lation scenarios.
Keywords:massive multiple input multiple output (MIMO)  antenna selection  power allocation  quantum chemical reaction optimization  
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