系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (12): 3843-3849.doi: 10.12305/j.issn.1001-506X.2022.12.30

• 通信与网络 • 上一篇    

基于神经网络的高并行大规模MIMO信号检测算法

许耀华*, 朱成龙, 王翊, 蒋芳, 丁梦琴, 王慧平   

  1. 安徽大学计算智能与信号处理教育部重点实验室, 安徽 合肥 230601
  • 收稿日期:2021-09-09 出版日期:2022-11-14 发布日期:2022-11-24
  • 通讯作者: 许耀华
  • 作者简介:许耀华(1976—), 男, 副教授, 硕士, 主要研究方向为信息与通信系统|朱成龙(1997—), 男, 硕士研究生, 主要研究方向为5G通信信号处理、人工智能|王翊(1983—), 男, 讲师, 博士,主要研究方向为移动通信网络、通信信号处理|蒋芳(1981—), 女, 讲师, 博士,主要研究方向为6G通信信号处理|丁梦琴(1996—), 女, 硕士研究生, 主要研究方向为5G移动通信|王慧平(1995—), 女, 硕士研究生, 主要研究方向为车联网通信
  • 基金资助:
    中国科学院上海微系统与信息技术研究所无线传感网与通信研究所(20190911)

Neural network-based algorithm for high-parallelism massive MIMO signal detection

Yaohua XU*, Chenglong ZHU, Yi WANG, Fang JANG, Mengqin DING, Huiping WANG   

  1. Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230601, China
  • Received:2021-09-09 Online:2022-11-14 Published:2022-11-24
  • Contact: Yaohua XU

摘要:

随着5G和未来移动无线网络的不断发展, 大规模多输入多输出(multiple input multiple output, MIMO)是其中的关键技术之一。随着天线数目的不断增加, 给接收机的设计带来更高的挑战, 复杂度过高的检测算法在实际中难以应用。本文将一种高并行(high-parallelism, HP)检测算法展开到神经网络中, 单层神经网络基于该算法的每次迭代, 并将其与可训练的权重参数和非线性神经单元相结合, 提出基于网络结构HP-Net的方法。通过训练HP-Net得到最优可训练参数, 进而提高检测性能。实验结果表明, 所提方法相对传统最小均方误差(minimum mean square error, MMSE)算法复杂度更低, 并能够得到更低的误码率; 同时相对HP并行检测算法误码率性能更优。

关键词: 大规模多输入多输出, 深度神经网络, 信号检测

Abstract:

Massive multiple input multiple output (MIMO) is one of the key technologies, as 5G and future mobile wireless networks continue to evolve. With the increasing number of antennas, the design of receivers has brought a huge challenge, and detection algorithms with too much complexity are difficult to be applied in practice. In this paper, a high-parallelism (HP) detection algorithm is developed into a neural network, the single-layer neural network is based on each iteration of this algorithm, which is combined with trainable weighted parameters and nonlinear neural units, and the network structure HP-Net is proposed. Optimal trainable parameters are obtained by training the HP-Net, which in turn improves the detection performance. The experimental results show that the paper method is less complex and can obtain lower bit error rate (BER) than the traditional minimum mean square error (MMSE) algorithm, and has better BER performance than the HP detection algorithm.

Key words: massive multiple input multiple output (MIMO), deep neural network (DNN), signal detection

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