计算机应用 ›› 2019, Vol. 39 ›› Issue (10): 3007-3012.DOI: 10.11772/j.issn.1001-9081.2019040638

• 网络与通信 • 上一篇    下一篇

基于改进共轭梯度的大规模多输入多输出预编码

白鹤, 刘紫燕, 张杰, 万培佩, 马珊珊   

  1. 贵州大学 大数据与信息工程学院, 贵阳 550025
  • 收稿日期:2019-04-16 修回日期:2019-06-28 出版日期:2019-10-10 发布日期:2019-08-21
  • 通讯作者: 刘紫燕
  • 作者简介:白鹤(1995-),女,吉林大安人,硕士研究生,主要研究方向:大规模MIMO预编码;刘紫燕(1974-),女,贵州都匀人,副教授,硕士,CCF会员,主要研究方向:无线通信系统、移动机器人、大数据挖掘分析;张杰(1995-),男,四川巴中人,硕士研究生,主要研究方向:移动机器人;万培佩(1994-),男,湖北安陆人,硕士研究生,主要研究方向:深度学习;马珊珊(1996-),女,贵州遵义人,硕士研究生,主要研究方向:信道估计。
  • 基金资助:
    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)。

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

BAI He, LIU Ziyan, ZHANG Jie, WAN Peipei, MA Shanshan   

  1. College of Big Data and Information Engineering, Guizhou University, Guiyang Guizhou 550025, China
  • Received:2019-04-16 Revised:2019-06-28 Online:2019-10-10 Published:2019-08-21
  • Supported by:
    This work is partially supported by the Natural Science Foundation of China (61863006), the Science and Technology Foundation of Guizhou Province ([2016]1054), the Joint Natural Science Foundation of Guizhou Province (LH[2017]7226), the Key Project of Science and Technology Plan of Guizhou Province ([2019]1416),

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26 dB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%。

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

Abstract: 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) is 26 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%.

Key words: Massive Multi-Input Multi-Output (Massive MIMO), linear precoding, conjugate gradient, Symmetric Successive Over Relaxation (SSOR)

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