{"title":"RIS-Aided Near-Field Localization and Channel Estimation for the Terahertz System","authors":"Yijin Pan;Cunhua Pan;Shi Jin;Jiangzhou Wang","doi":"10.1109/JSTSP.2023.3285431","DOIUrl":null,"url":null,"abstract":"The affordable hardware cost of ultra-large (XL) reconfigurable intelligent surfaces (RIS) renders them attractive solutions for the performance enhancement of localization and communication systems. However, XL-RIS results in near-field propagation channels, especially for the high-frequency terahertz (THz) communication system, which poses significant challenges for localization and channel estimation. In this article, we focus on the spherical wavefront propagation in the near field of the THz system with the assistance of a RIS. A near-field channel estimation and localization (NF-JCEL) algorithm is proposed based on the derived second-order Fresnel approximation of the near-field channel model. To be specific, we carefully devise a down-sampled Toeplitz covariance matrix, which enables the decoupling and separate estimation of user equipment (UE) distances and angles of arrival (AoAs). Using the sub-space based method and one-dimensional search, we estimate the angles of arrival (AoAs) and user equipment (UE) distances. The channel attenuation coefficients are obtained through the least square (LS) method. To alleviate the impact of THz channel fading peaks caused by molecular absorption, estimates on multiple sub-bands are utilized for location estimation. Simulation results validate the superiority of the proposed NF-JCEL algorithm to the conventional far-field algorithm and show that higher resolution accuracy can be obtained by the proposed algorithm.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":"17 4","pages":"878-892"},"PeriodicalIF":8.7000,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10149471/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 9
Abstract
The affordable hardware cost of ultra-large (XL) reconfigurable intelligent surfaces (RIS) renders them attractive solutions for the performance enhancement of localization and communication systems. However, XL-RIS results in near-field propagation channels, especially for the high-frequency terahertz (THz) communication system, which poses significant challenges for localization and channel estimation. In this article, we focus on the spherical wavefront propagation in the near field of the THz system with the assistance of a RIS. A near-field channel estimation and localization (NF-JCEL) algorithm is proposed based on the derived second-order Fresnel approximation of the near-field channel model. To be specific, we carefully devise a down-sampled Toeplitz covariance matrix, which enables the decoupling and separate estimation of user equipment (UE) distances and angles of arrival (AoAs). Using the sub-space based method and one-dimensional search, we estimate the angles of arrival (AoAs) and user equipment (UE) distances. The channel attenuation coefficients are obtained through the least square (LS) method. To alleviate the impact of THz channel fading peaks caused by molecular absorption, estimates on multiple sub-bands are utilized for location estimation. Simulation results validate the superiority of the proposed NF-JCEL algorithm to the conventional far-field algorithm and show that higher resolution accuracy can be obtained by the proposed algorithm.
期刊介绍:
The IEEE Journal of Selected Topics in Signal Processing (JSTSP) focuses on the Field of Interest of the IEEE Signal Processing Society, which encompasses the theory and application of various signal processing techniques. These techniques include filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals using digital or analog devices. The term "signal" covers a wide range of data types, including audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and others.
The journal format allows for in-depth exploration of signal processing topics, enabling the Society to cover both established and emerging areas. This includes interdisciplinary fields such as biomedical engineering and language processing, as well as areas not traditionally associated with engineering.