Tensor Decomposition-Based Channel Estimation and Sensing for Millimeter Wave MIMO-OFDM V2I Systems

doi:10.12142/ZTECOM.202403008

ZTE Communications ›› 2024, Vol. 22 ›› Issue (3): 56-68.DOI: 10.12142/ZTECOM.202403008

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收稿日期:2024-03-01 出版日期:2024-09-25 发布日期:2024-09-29

Tensor Decomposition-Based Channel Estimation and Sensing for Millimeter Wave MIMO-OFDM V2I Systems

WANG Jilin¹(), ZENG Xianlong¹, YANG Yonghui²^,³, PENG Lin²^,³, LI Lingxiang¹

^1.National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China
^2.ZTE Corporation, Shenzhen 518057, China
^3.State Key Laboratory of Mobile Network and Mobile Multimedia Technology, Shenzhen 518055, China

Received:2024-03-01 Online:2024-09-25 Published:2024-09-29
About author:WANG Jilin ( jilinwang@std.uestc.edu.cn) has been working toward his PhD degree with National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China (UESTC) since 2021. His current research interests include compressed sensing and millimeter-wave communication.
ZENG Xianlong received his BS degree from University of Electronic Science and Technology of China (UESTC) in 2018. He has been working toward his MS degree with National Key Laboratory of Wireless Communications, UESTC since 2022. His current research interests include integrated sensing and communication (ISAC).
YANG Yonghui received his BS degree in information countermeasure techniques and MS degree in electronic information engineering from Xidian University, China in 2006 and 2010, respectively. Since 2010, he has been with ZTE Corporation, where his research interests focus on wireless communications. His current research interests include 5G and 6G technology, and millimeter-wave and terahertz communication.
PENG Lin received his BS degree in information engineering and MS degree in electromagnetic field and microwave techniques from Nanjing University of Science and Technology, China in 2004 and 2006, respectively. Since 2006, he has been with ZTE Corporation, where he focuses on the research of wireless communications. His current research interests include beyond 5G and 6G technology, millimeter-wave and terahertz communications and intelligent reflecting surface for wireless applications.
LI Lingxiang received her BS degree from Central South University, China in 2010, and her PhD degree from University of Electronic Science and Technology of China (UESTC) in 2017, all in electrical engineering. She was a visiting PhD student under supervision of Prof. Athina P. PETROPULU at Rutgers, The State University of New Jersey, USA during 2015–2016. Dr. LI is now an associate professor with the National Key Lab of Science and Technology on Communications, UESTC. Her research interests include THz communications and Integrated Sensing and Communications (ISAC).

摘要/Abstract

Abstract:

An integrated sensing and communication (ISAC) scheme for a millimeter wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) Vehicle-to-Infrastructure (V2I) system is presented, in which both the access point (AP) and the vehicle are equipped with large antenna arrays and employ hybrid analog and digital beamforming structures to compensate the path loss, meanwhile compromise between hardware complexity and system performance. Based on the sparse scattering nature of the mmWave channel, the received signal at the AP is organized to a four-order tensor by the introduced novel frame structure. A CANDECOMP/PARAFAC (CP) decomposition-based method is proposed for time-varying channel parameter extraction, including angles of departure/arrival (AoDs/AoAs), Doppler shift, time delay and path gain. Then leveraging the estimates of channel parameters, a nonlinear weighted least-square problem is proposed to recover the location accurately, heading and velocity of vehicles. Simulation results show that the proposed methods are effective and efficient in time-varying channel estimation and vehicle sensing in mmWave MIMO-OFDM V2I systems.

Key words: MIMO-OFDM Vehicle-to-Infrastructure (V2I) systems, ISAC, time-varying channel estimation, CANDECOMP/PARAFAC (CP) decomposition

. [J]. ZTE Communications, 2024, 22(3): 56-68.

WANG Jilin, ZENG Xianlong, YANG Yonghui, PENG Lin, LI Lingxiang. Tensor Decomposition-Based Channel Estimation and Sensing for Millimeter Wave MIMO-OFDM V2I Systems[J]. ZTE Communications, 2024, 22(3): 56-68.

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