基于改进共轭梯度的大规模多输入多输出预编码  

Precoding based on improved conjugate gradient algorithm in massive multi-input multi-output system

作　　者：白鹤 刘紫燕 张杰 万培佩 马珊珊 BAI He;LIU Ziyan;ZHANG Jie;WAN Peipei;MA Shanshan(College of Big Data and Information Engineering, Guizhou University, Guiyang Guizhou 550025, China)

机构地区：[1]贵州大学大数据与信息工程学院

出　　处：《计算机应用》2019年第10期3007-3012,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61863006);贵州省科学技术基金资助项目(黔科合基础[2016]1054);贵州省联合资金资助项目(黔科合LH字[2017]7226号);贵州省科技计划重点项目([2019]1416);贵州大学2017年度学术新苗培养及创新探索专项(黔科合平台人才[2017]5788)~~

摘　　要：针对大规模多输入多输出(Massive MIMO)系统下行链路预编码实现复杂、线性预编码矩阵求逆困难等问题,提出一种基于对称逐步超松弛预处理共轭梯度法(SSOR-PCG)的低复杂度预编码算法。该算法在共轭梯度(PCG)算法的基础上,采用对称逐步超松弛分裂(SSOR)算法对矩阵进行预处理以降低矩阵的条件数,达到提高预编码算法收敛速度、降低复杂度的目的。仿真结果表明:与PCG算法相比,所提出的SSOR-PCG预编码算法运行时间缩短约88.93%,在信噪比为26dB时已收敛;与迫零预编码算法相比,所提算法迭代2次即可获得与迫零预编码算法相近的系统容量性能,复杂度降低约一个数量级,误码率降低约49.94%。To solve the problems of high complexity of precoding and difficulty of linear matrix inversion in downlink Massive Multi-Input Multi-Output (Massive MIMO) system, a precoding algorithm based on low- complexity Symmetric Successive Over Relaxation Preconditioned Conjugate Gradient (SSOR-PCG) was proposed. Based on preconditioned Conjugate Gradient Precoding (PCG) algorithm, a Symmetric Successive Over Relaxation (SSOR) algorithm was used to preprocess the matrix to reduce its condition number, accelerating the convergence speed and the decreasing the complexity. Simulation results demonstrate that compared with PCG algorithm, the proposed algorithm has running time of around 88.93% shortened and achieves convergence when the Signal-to-Noise Ratio (SNR) is26 dB. Furthermore, compared to zero-forcing precoding algorithm, the proposed algorithm requires only two iterations capacity-approaching performance,the overall complexity is reduced by one order of magnitude, and the bit error rate is decreased by about 49.94%.

关 键 词：大规模多输入多输出 线性预编码 共轭梯度 对称逐步超松弛 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]