毫米波MIMO系统中基于自适应梯度算法的混合预编码

张煜; 张治; 董晓岱

doi:10.11959/j.issn.1000-436x.2021207

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毫米波MIMO系统中基于自适应梯度算法的混合预编码

学术论文 | 更新时间：2024-06-05

- 毫米波MIMO系统中基于自适应梯度算法的混合预编码
- Adaptive gradient algorithm for hybrid precoding in mmWave MIMO system
- 通信学报 2021年42卷第10期页码：95-105
- 作者机构：
  
  1. 北京邮电大学信息与通信工程学院，北京 100876
  2. 维多利亚大学电子与计算机工程学院，维多利亚 V8W 3P6
- 作者简介：
  
  [ "张煜（1992- ），男，山东滨州人，北京邮电大学博士生，主要研究方向为大规模MIMO、混合预编码" ]
  [ "张治（1977- ），男，河北安平人，博士，北京邮电大学教授、博士生导师，主要研究方向为无线信号智能检测与高效通信的相关理论、关键技术与系统设计等" ]
  [ "董晓岱（1970- ），女，河南洛阳人，博士，维多利亚大学教授、博士生导师，主要研究方向为 5G、毫米波通信、太赫兹通信等" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2019YFC1511302);国家自然科学基金资助项目(61629101)
- DOI：10.11959/j.issn.1000-436x.2021207
  中图分类号： TN92
- 网络首发：2021-10，
  
  纸质出版：2021-10-25
- 稿件说明：
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张煜, 张治, 董晓岱. 毫米波MIMO系统中基于自适应梯度算法的混合预编码[J]. 通信学报, 2021,42(10):95-105.

Yu ZHANG, Zhi ZHANG, Xiaodai DONG. Adaptive gradient algorithm for hybrid precoding in mmWave MIMO system[J]. Journal on Communications, 2021, 42(10): 95-105.
张煜, 张治, 董晓岱. 毫米波MIMO系统中基于自适应梯度算法的混合预编码[J]. 通信学报, 2021,42(10):95-105. DOI： 10.11959/j.issn.1000-436x.2021207.

Yu ZHANG, Zhi ZHANG, Xiaodai DONG. Adaptive gradient algorithm for hybrid precoding in mmWave MIMO system[J]. Journal on Communications, 2021, 42(10): 95-105. DOI： 10.11959/j.issn.1000-436x.2021207.

摘要

为了降低毫米波MIMO系统中现有交替最小混合预编码算法和基于动量梯度下降的在线学习混合预编码方案的复杂度，针对单用户通信场景，重新考虑混合预编码器的设计问题，提出一种等效的单隐藏层神经网络架构。在该架构下，数字预编码矩阵和模拟预编码矩阵的每一个组成元素可等效为单隐藏层神经网络的连接权值，其最优解可通过神经网络中的权值训练方法获得。在此基础上，结合反向传播算法，提出一种基于自适应梯度反向传播的混合预编码机制。进一步地，将所提算法扩展到多用户通信场景。仿真结果表明，在单用户场景和多用户场景下，所提算法可实现的频谱效率均接近全数字预编码，同时复杂度低于现有的基于交替最小的混合预编码算法和基于动量梯度下降的在线学习混合预编码方案。

Abstract

To reduce the complexity of existing hybrid precoding algorithms based on alternating minimization (AltMin) and online learning via gradient descent with momentum in mmWave MIMO systems

aiming at the single-user scenario

the problem of designing the hybrid precoder was reconsidered and an equivalent single hidden layer neural network was proposed.Under the new architecture

the elements of the digital and analog precoder were equivalent to the connecting weights of a single hidden layer neural network

and their optimal solution could be obtained via the weights training method.Inspired by the back propagation (BP) algorithm in feed forward neural networks

an adaptive gradient (AG)-based BP algorithm for hybrid precoding was proposed.Furthermore

the proposed algorithm was extended to the multi-user scenario.The numerical results show that the proposed algorithm achieves approximately the same spectral efficiency as the fully-digital precoding in both the single-user and multi-user scenarios

while has lower complexity than the existing AltMin-based hybrid precoding algorithms and online learning hybrid precoding based on gradient descent with momentum.

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Keywords

references

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