Journal of Xidian University ›› 2023, Vol. 50 ›› Issue (3): 19-30.doi: 10.19665/j.issn1001-2400.2023.03.002

• Special Issue on 6G Key Technologies for IT3.0 Based on the Integration of Communication,Sensing and Computing • Previous Articles     Next Articles

ResNet enabled joint channel estimation and signal detection for OTFS

ZHOU Shuo1,2,3(),ZHOU Yiqing1,2,3(),ZHANG Chong1,2,3(),XING Wang1,2,3()   

  1. 1. State Key Lab of Processors,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
    2. School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China
    3. Beijing Key Laboratory of Mobile Computing and Pervasive Device,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2022-12-16 Online:2023-06-20 Published:2023-10-13
  • Contact: Yiqing ZHOU E-mail:zhoushuo20s@ict.ac.cn;zhouyiqing@ict.ac.cn;zhangchong@ict.ac.cn;xingwang@ict.ac.cn

Abstract:

Orthogonal time frequency space (OTFS) modulation can realize reliable broadband communication at a high doppler frequency offset,which is one of the potential application technologies in the 6G communication-sensing-computing scenario.In order to solve the problems of high complexity and limited performance of the receiver in this system,a joint channel estimation and signal detection algorithm based on modified ResNet is proposed,with the transmission symbol information recovered directly without obtaining explicit channel information.According to the stability of the delay doppler domain channel,deep learning technology is introduced into the receiver design,and a lightweight residual neural network model that can fully extract the signal features is designed by using the embedded pilot data frame structure.It can directly fit the input-output relationship of delay doppler domain signals to achieve implicit channel estimation and complete signal detection.In the joint design,the optimal network model is obtained by off-line training with the data collected in the actual communication link,which can be used for on-line detection.Meanwhile,the joint optimization of channel estimation and signal detection is realized with the help of an error back propagation mechanism and gradient descent criterion,which effectively improves the communication performance.Simulation results show that the proposed scheme has better robustness and good generalization compared with the traditional receiver algorithm,which not only reduces the algorithm complexity,but also improves the BER performance by about 2dB.

Key words: orthogonal time frequency space modulation, deep learning, channel estimation, signal detection

CLC Number: 

  • TN911

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