Enhanced Channel Estimation for OTFS-Assisted ISAC in Vehicular Networks: A Deep Learning Approach

Xiaoqi Zhang1; Hongjia Huang2; Long Tan3; Weijie Yuan1; Chang Liu2

10.23919/WiOpt58741.2023.10349836

2023

2690-3334

979-8-3503-4158-4

2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

703-707

24-27 Aug. 2023

Singapore, Singapore

This paper explores an orthogonal time frequency space (OTFS)-assisted integrated sensing and communication (ISAC) system in vehicular networks. We present a deep learning (DL)-based framework for the OTFS-assisted ISAC system, leveraging the advantages offered by the Delay-Doppler representation of the time-variant channel. The communication channel matrix is utilized within the framework to infer motion parameters, thereby enabling the establishment of an effective transmission protocol. Therefore, it is crucial to design a channel estimation method that simultaneously fulfills both sensing and communication performance requirements. To this end, a DL-based channel estimation approach is designed to obtain accurate channel state information (CSI), due to the powerful capability of neural networks [1]. Specifically, we model the channel estimation as a denoising problem from the embedded pilot scheme and employ a self-adaptive threshold submodule to eliminate irrelevant features. Finally, simulation results demonstrate that our proposed method can obtain accurate CSI with the available sensing performance.

Orthogonal time frequency space (OTFS) in-tegrated sensing and communication (ISAC) deep learning vehicular networks

第一

EI

20240715543155

Channel estimation ; Deep learning ; Frequency estimation

Ergonomics and Human Factors Engineering:461.4

IEEE

1.Southern University of Science and Technology, Shenzhen, China
2.The Hong Kong Polytechnic University, Hong Kong, SAR, China
3.Taiyuan University of Technology, Taiyuan, China

Xiaoqi Zhang,Hongjia Huang,Long Tan,et al. Enhanced Channel Estimation for OTFS-Assisted ISAC in Vehicular Networks: A Deep Learning Approach[C],2023:703-707.