Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (9): 2687-2693.DOI: 10.11772/j.issn.1001-9081.2020111779

Special Issue: 网络与通信

• Network and communications • Previous Articles     Next Articles

Deep learning-based joint channel estimation and equalization algorithm for C-V2X communications

CHEN Chengrui1, SUN Ning2, HE Shibiao1, LIAO Yong2   

  1. 1. School of Electronic Information, Chongqing Institute of Engineering, Chongqing 400056, China;
    2. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
  • Received:2020-11-16 Revised:2021-02-25 Online:2021-09-10 Published:2021-05-08
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61501066), the Natural Science Foundation of Chongqing (cstc2019jcyj-msxmX0017).


陈成瑞1, 孙宁2, 何世彪1, 廖勇2   

  1. 1. 重庆工程学院 电子信息学院, 重庆 400056;
    2. 重庆大学 微电子与通信工程学院, 重庆 400044
  • 通讯作者: 廖勇
  • 作者简介:陈成瑞(1984-),男,山东济宁人,高级工程师,硕士,主要研究方向:工业互联网、智能控制、智能信号与信息处理;孙宁(1995-),男,河南长垣人,硕士研究生,研究方向:智能信号与信息处理;何世彪(1963-),男,安徽安庆人,教授,博士,主要研究方向:宽带无线通信、智能信号与信息处理;廖勇(1982-),男,四川自贡人,副研究员,博士,CCF杰出会员,主要研究方向:移动通信、人工智能。
  • 基金资助:

Abstract: In order to effectively improve the Bit Error Rate (BER) performance of communication system without significantly increasing the computational complexity, a deep learning based joint channel estimation and equalization algorithm named V-EstEqNet was proposed for Cellular-Vehicle to Everything (C-V2X) communication system by using the powerful ability of deep learning in data processing. Different from the traditional algorithms, in which channel estimation and equalization in the communication system reciever were carried out in two stages respectively, V-EstEqNet considered them jointly, and used the deep learning network to directly correct and restore the received data, so that the channel equalization was completed without explicit channel estimation. Specifically, a large number of received data were used to train the network offline, so that the channel characteristics superimposed on the received data were learned by the network, and then these characteristics were utilized to recover the original transmitted data. Simulation results show that the proposed algorithm can track channel characteristics more effectively in different speed scenarios. At the same time, compared with the traditional channel estimation algorithms (Least Squares (LS) and Linear Minimum Mean Square Error (LMMSE)) combining with the traditional channel equalization algorithms (Zero Forcing (ZF) equalization algorithm and Minimum Mean Square Error (MMSE) equalization algorithm), the proposed algorithm has a maximum BER gain of 6 dB in low-speed environment and 9 dB in high-speed environment.

Key words: Vehicle to Everything (V2X), Celluar-V2X (C-V2X), channel estimation, channel equalization, deep learning

摘要: 为了在不显著提升计算复杂度的情况下,有效提升通信系统的误码率(BER)性能,利用深度学习在数据处理方面的强大能力,提出一种面向基于蜂窝网络的车联网(C-V2X)通信的基于深度学习的联合信道估计与均衡算法——V-EstEqNet。与传统算法分两个阶段分别进行信道估计与均衡不同,V-EstEqNet将通信系统接收机中的信道估计与信道均衡进行联合考虑,并利用深度学习网络直接对接收数据进行校正和恢复,无须进行显式的信道估计环节即可完成信道均衡。具体而言,首先利用大量的接收数据对网络进行离线训练,使网络学习到叠加在接收数据中的信道特性;然后利用该特性恢复原始的发送数据。仿真实验结果表明,在不同的速度场景下,所提算法可以更加有效地追踪信道特性;同时,相较于传统信道估计算法(最小二乘法(LS)和线性最小均方误差法(LMMSE))配合传统信道均衡算法(迫零(ZF)均衡算法和最小均方误差(MMSE)均衡算法),所提算法在低速环境下有最高有6 dB的BER增益,在高速环境下最高有9 dB的BER增益。

关键词: 车联网, 基于蜂窝网络的车联网, 信道估计, 信道均衡, 深度学习

CLC Number: