电信科学 ›› 2024, Vol. 40 ›› Issue (3): 39-52.doi: 10.11959/j.issn.1000-0801.2024069

• 研究与开发 • 上一篇    

基于复数生成对抗网络的5G OFDM信道估计方法

陆元智1, 魏祥麟2, 于龙2, 姚昌华1   

  1. 1 南京信息工程大学电子与信息工程学院，江苏 南京 210044
    2 国防科技大学第六十三研究所，江苏 南京 210007
  • 修回日期:2023-12-22 出版日期:2024-03-01 发布日期:2024-03-01
  • 作者简介:陆元智（1997- ），男，南京信息工程大学硕士生，主要研究方向为深度学习、信道估计
    魏祥麟（1985- ），男，博士，国防科技大学第六十三研究所副研究员，主要研究方向为频谱智能计算
    于龙（1981- ），男，博士，国防科技大学第六十三研究所高级工程师，主要研究方向为通信抗干扰、智能通信博弈
    姚昌华（1982- ），男，博士，南京信息工程大学教授，主要研究方向为智能无人集群、智能无线通信
  • 基金资助:
    国家自然科学基金资助项目(61971439);国家自然科学基金资助项目(U22B2002);江苏省自然科学基金资助项目(BK20191329);通信抗干扰全国重点实验室基础科研创新基金（稳定支持）项目(IFN20230207)

5G OFDM channel estimation method based on complexvalued generative adversarial network

Yuanzhi LU1, Xianglin WEI2, Long YU2, Changhua YAO1   

  1. 1 School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
    2 The 63rd Research Institute, National University of Defense Technology, Nanjing 210007, China
  • Revised:2023-12-22 Online:2024-03-01 Published:2024-03-01
  • Supported by:
    The National Natural Science Foundation of China(61971439);The National Natural Science Foundation of China(U22B2002);The Natural Science Foundation of Jiangsu Province of China(BK20191329);The National Key Laboratory of Wireless Communications Foundation(IFN20230207)

在线阅读

7

PDF下载

34

摘要：

准确的信道估计能够显著地降低误码率（bit error rate，BER），提高无线通信效率和质量，是5G OFDM通信系统接收机设计的关键环节之一。基于最小二乘（least square，LS）法和基于最小均方差（minimum mean square error，MMSE）的信道估计方法利用系统稀疏性计算信道响应矩阵，但LS算法计算精度较低，而MMSE算法计算量过大。为提升估计精度，业界设计了基于深度学习的信道估计方法。然而，现有的深度学习方法将复数矩阵拆分成实部和虚部，没有充分提取信道中的复数特征，造成估计的信道响应矩阵出现失真。为此，设计了一种基于复数的生成对抗网络模型，充分提取信号的复数特征，从而更准确地估计5G新空口（new radio，NR）标准的物理下行链路共享信道（physical downlink shared channel，PDSCH）的信道响应矩阵。为了验证所提方法的有效性，将所提方法分别与LS算法、实际信道估计、超分辨率神经网络、残差神经网络信道估计算法进行了对比分析。结果表明，当估计的信道响应矩阵与真实矩阵之间的均方差达到0.01时，采用所提方法实现的无线通信系统的信噪比高于现有方法5 dB左右。

关键词: 5G新NR, 信道估计, 物理下行链路共享信道, 复数神经网络, 生成对抗网络

Abstract:

Accurate channel estimation is a critical component in the design of 5G OFDM communication system receivers, since it can significantly reduce the bit error rate (BER), thus improving wireless communication efficiency and quality.Channel estimation methods based on least square (LS) and minimum mean square error (MMSE) effectively utilize the system’s sparsity, but LS algorithms face low computational precision, while MMSE algorithms suffer from high computational complexity.To promote the estimation accuracy, practitioners have presented several deep learning-based channel estimation methods.However, existing methods often split complex matrices into real and imaginary parts, failing to adequately capture the complex characteristics of the channel, leading to distortion in the estimated channel matrix.A complex-valued generative adversarial network (GAN) model that could fully extract the complex features of the signals was proposed, enabling accurate estimation of the channel matrix for the physical downlink shared channel (PDSCH) in the 5G new radio (NR) standard.To validate the effectiveness of the proposed method, the proposed method was compared with LS algorithms, actual channel estimation, super-resolution neural networks, and residual neural network channel estimation methods.Results show that when the mean square error between the estimated channel matrix and the true channel matrix is 0.01, the proposed method-based communication system has a signal-to-noise ratio (SNR) that is 5 dB higher than existing ones.

Key words: 5G new radio, channel estimation, PDSCH, complex valued neural network, generative adversarial network

中图分类号: 

No Suggested Reading articles found!