A Novel Joint Channel Estimation and Symbol Detection Receiver for Orthogonal Time Frequency Space in Vehicular Networks

Xiaoqi ZHANG, Haifeng WEN, Ziyu YAN, Weijie YUAN*, Jun WU, Zhongjie LI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

2 Citations (Scopus)

Abstract

A vehicular network embodies a specialized variant of wireless network systems, characterized by its capability to facilitate inter-vehicular communication and connectivity with the encompassing infrastructure. With the rapid development of wireless communication technology, high-speed and reliable communication has become increasingly important in vehicular networks. It has been demonstrated that orthogonal time frequency space (OTFS) modulation proves effective in addressing the challenges posed by high-mobility environments, as it transforms the time-varying channels into the delay-Doppler domain. Motivated by this, in this paper, we focus on the theme of integrated sensing and communication (ISAC)-assisted OTFS receiver design, which aims to perform sensing channel estimation and communication symbol detection. Specifically, the estimation of the sensing channel is accomplished through the utilization of a deep residual denoising network (DRDN), while the communication symbol detection is performed by orthogonal approximate message passing (OAMP) processing. The numerical results demonstrate that the proposed ISAC system exhibits superior performance and robustness compared to traditional methods, with a lower complexity as well. The proposed system has great potential for future applications in wireless communication systems, especially in challenging scenarios with high mobility and interference.
Original languageEnglish
Article number1358
Number of pages16
JournalEntropy
Volume25
Issue number9
Early online dateSept 2023
DOIs
Publication statusPublished - 20 Sept 2023
Externally publishedYes

Bibliographical note

This work is supported in part by National Natural Science Foundation of China under Grant 62101232, and in part by the Guangdong Provincial Natural Science Foundation under Grant 2022A1515011257.

Keywords

  • deep learning
  • integrated sensing and communication (ISAC)
  • joint channel estimation and symbol detection
  • message passing (MP)
  • orthogonal time frequency space (OTFS)

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