{"title":"Model-Driven Channel Estimation for MIMO Monostatic Backscatter System With Deep Unfolding","authors":"Yulin Zhou;Xiaoting Li;Xianmin Zhang;Xiaonan Hui;Yunfei Chen","doi":"10.1109/OJCOMS.2024.3479234","DOIUrl":null,"url":null,"abstract":"Monostatic backscatter has garnered significant interest due to its distinct benefits in low-cost passive sensing. Observing and sensing with backscatter necessitates determining the phase and amplitude of the backscatter channel to identify the state of the target of interest. In the detection of multiple targets, colliding signals can distort the backscatter channel, complicating channel state recovery. It becomes even more challenging when multiple backscattering devices (BDs) are used. This paper proposes a novel channel estimation scheme to tackle the challenge, which is applied to a monostatic backscatter communication system with multiple reader antennas (RAs) and backscatter devices. Specifically, we propose a backscatter communication model and subsequently develop a de-interfering channel estimation framework that considers the ambient interference in the channel, named model-driven unfolded channel estimation (MUCE). To validate the effectiveness and advantages of the MUCE method, it is compared with the least square (LS) algorithm and convolutional neural network (CNN). The results prove that MUCE requires lower computational costs for the same channel estimation performance and achieves an optimal balance between estimation performance and computational expense.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10715725","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Communications Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10715725/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Monostatic backscatter has garnered significant interest due to its distinct benefits in low-cost passive sensing. Observing and sensing with backscatter necessitates determining the phase and amplitude of the backscatter channel to identify the state of the target of interest. In the detection of multiple targets, colliding signals can distort the backscatter channel, complicating channel state recovery. It becomes even more challenging when multiple backscattering devices (BDs) are used. This paper proposes a novel channel estimation scheme to tackle the challenge, which is applied to a monostatic backscatter communication system with multiple reader antennas (RAs) and backscatter devices. Specifically, we propose a backscatter communication model and subsequently develop a de-interfering channel estimation framework that considers the ambient interference in the channel, named model-driven unfolded channel estimation (MUCE). To validate the effectiveness and advantages of the MUCE method, it is compared with the least square (LS) algorithm and convolutional neural network (CNN). The results prove that MUCE requires lower computational costs for the same channel estimation performance and achieves an optimal balance between estimation performance and computational expense.
期刊介绍:
The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023.
The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include:
Systems and network architecture, control and management
Protocols, software, and middleware
Quality of service, reliability, and security
Modulation, detection, coding, and signaling
Switching and routing
Mobile and portable communications
Terminals and other end-user devices
Networks for content distribution and distributed computing
Communications-based distributed resources control.