基于粒子群优化算法的5G波束权值寻优方法

5G beamforming weight optimization method based on particle swarm optimization algorithm

为解决高层建筑物室内5G信号覆盖不足、优化难度大的问题,提出基于粒子群优化(particle swarm optimization,PSO)算法的5G波束权值寻优方法。通过采集终端数据将网络解耦成多个子网切片,对单小区权值库进行规划和仿真,构建多小区权值组变量以及适应度函数。最后,采用PSO算法求解多小区波束权值组局部最优值。测试结果表明,平均参考信号接收功率(reference signal receiving power,RSRP)提升8.7%,平均信干噪比(signal to interference plus noise ratio,SINR)提升17.5%,下行速率提升27.3%。所提方法在5G天线波束权值寻优上具有低成本、高效率、智能化的优势,可以改善高层室内用户的感知。

In order to solve the problems of insufficient indoor 5G signal coverage and difficulty in optimization of high-rise buildings,the 5G beamforming weight optimization method based on particle swarm optimization(PSO)algorithm was proposed.Decoupling and reducing the network dimension into multiple subnet slices by collecting the data reported by the terminal,and the single cell weight database was planned and simulated to create the multi-cells weights group and fitness function.Finally,the PSO algorithm was adopted to obtain the local optimal weight group of the multi-cells.The test results show that the reference signal receiving power(RSRP)is improved by 8.7%,the signal to interference plus noise ratio(SINR)is improved by 17.5%,the downlink throughput is promoted by 27.3%together.The proposed method has the advantages of low cost,high efficiency and intelligence,and can significantly improve the user perception in high-rise buildings.