基于修正牛顿法的大规模MIMO低复杂度混合预编码算法

胡博; 王安定; 魏贵义

doi:10.11959/j.issn.1000-0801.2023186

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基于修正牛顿法的大规模MIMO低复杂度混合预编码算法

研究与开发 | 更新时间：2024-06-05

- 基于修正牛顿法的大规模MIMO低复杂度混合预编码算法
- Low complexity hybrid precoding algorithm for massive MIMO based on modified Newton method
- 电信科学 2023年39卷第11期页码：80-95
- 作者机构：
- 作者简介：
  
  [ "胡博（1996- ），男，浙江工商大学信息与电子工程学院硕士生，主要研究方向为大规模MIMO、混合预编码" ]
  [ "王安定（1975- ），男，博士，浙江工商大学信息与电子工程学院副教授，主要研究方向为毫米波通信、空时信号处理以及大规模MIMO系统等下一代无线通信核心技术" ]
  [ "魏贵义（1973- ），男，博士，浙江工商大学信息与电子工程学院院长、教授，主要研究方向为无线通信、移动计算、云计算、社交网络、博弈论" ]
- 基金信息：
  
  浙江省自然科学基金资助项目;The Natural Science Foundation of Zhejiang Province(LY22F010013)
- DOI：10.11959/j.issn.1000-0801.2023186
  中图分类号： TN928
- 网络出版日期：2023-11，
  
  纸质出版日期：2023-11-20
- 稿件说明：
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胡博, 王安定, 魏贵义. 基于修正牛顿法的大规模MIMO低复杂度混合预编码算法[J]. 电信科学, 2023,39(11):80-95.

Bo HU, Anding WANG, Guiyi WEI. Low complexity hybrid precoding algorithm for massive MIMO based on modified Newton method[J]. Telecommunications science, 2023, 39(11): 80-95.
胡博, 王安定, 魏贵义. 基于修正牛顿法的大规模MIMO低复杂度混合预编码算法[J]. 电信科学, 2023,39(11):80-95. DOI： 10.11959/j.issn.1000-0801.2023186.

Bo HU, Anding WANG, Guiyi WEI. Low complexity hybrid precoding algorithm for massive MIMO based on modified Newton method[J]. Telecommunications science, 2023, 39(11): 80-95. DOI： 10.11959/j.issn.1000-0801.2023186.

摘要

针对大规模多输入多输出（multiple-input multiple-output，MIMO）系统，提出了一种基于修正牛顿（modified Newton，MN）法的相位跟踪算法，有效地解决了传统高性能混合预编码方案中的高计算复杂度问题。该算法从子维度向量恢复的角度优化模拟预编码矩阵。在每个子维度优化中，采用相位跟踪方法将模拟预编码向量的恢复转化为无约束的非线性优化问题，并利用 MN 法进行求解。同时，应用 Gerschgorin’s Disk 定理和Hermitian矩阵分块求逆引理，分别降低了MN法中计算修正因子以及Hessian矩阵求逆的计算复杂度。实验结果表明，与仿真中几种传统的高性能混合预编码方案相比，所提算法具有更高的频谱效率和更低的计算复杂度。

Abstract

A phase tracking algorithm was proposed for massivemultiple-input multiple-output(MIMO) systems based on the modified Newton (MN) method

which effectively reduced the high computational complexity in traditional high-performance hybrid precoding schemes.The algorithm optimized the analog precoding matrix from the perspective ofsub-dimensional vector recovery.In each sub-dimension optimization

the phase tracking method was used to transform the recovery of the analog precoding vectors into an unconstrained nonlinear optimization problem

which was then solved using the MN method.Concurrently

this strategy led to a marked reduction in the computational intricacy pertaining to both the computation of correction factors and the inversion of the Hessian matrix within the framework of the MN method.This was achieved through the insightful incorporation of Gerschgorin’s Disk theorem and the Hermitian matrix block-inverse lemma.Simulation results show that the proposed algorithm has higher spectral efficiency and lower computational complexity than several conventional high-performance hybrid precoding schemes.

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