A Heterogeneous 6G Networked Sensing Architecture With Active and Passive Anchors

被引:1
作者
Wang, Qipeng [1 ]
Liu, Liang [1 ]
Zhang, Shuowen [1 ]
Di, Boya [2 ]
Lau, Francis C. M. [1 ]
机构
[1] Hong Kong Polytech Univ, Dept Elect & Elect Engn, Hong Kong, Peoples R China
[2] Peking Univ, Dept Elect, Beijing 100871, Peoples R China
基金
中国国家自然科学基金;
关键词
Integrated sensing and communication (ISAC); intelligent reflecting surface (IRS); networked sensing; 6G; data association; COMMUNICATION; RADAR;
D O I
10.1109/TWC.2024.3363180
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the future 6G integrated sensing and communication (ISAC) cellular systems, networked sensing is a promising technique that can leverage the cooperation among the base stations (BSs) to perform high-resolution localization. However, a dense deployment of BSs to fully reap the networked sensing gain is not a cost-efficient solution in practice. Motivated by the advance in the intelligent reflecting surface (IRS) technology for 6G communication, this paper examines the feasibility of deploying the low-cost IRSs to enhance the anchor density for networked sensing. Specifically, we propose a novel heterogeneous networked sensing architecture, which consists of both the active anchors, i.e., the BSs, and the passive anchors, i.e., the IRSs. Under this framework, the BSs emit the orthogonal frequency division multiplexing (OFDM) communication signals in the downlink for localizing the targets based on their echoes reflected via/not via the IRSs. However, there are two challenges for using passive anchors in localization. First, it is impossible to utilize the round-trip signal between a passive IRS and a passive target for estimating their distance. Second, before localizing a target, we do not know which IRS is closest to it and serves as its anchor. In this paper, we show that the distance between a target and its associated IRS can be indirectly estimated based on the length of the BS-target-BS path and the BS-target-IRS-BS path. Moreover, we propose an efficient data association method to match each target to its associated IRS. Numerical results are given to validate the feasibility and effectiveness of our proposed heterogeneous networked sensing architecture with both active and passive anchors.
引用
收藏
页码:9502 / 9517
页数:16
相关论文
共 29 条
  • [1] Reconfigurable Intelligent Surfaces: A signal processing perspective with wireless applications
    Bjornson, Emil
    Wymeersch, Henk
    Matthiesen, Bho
    Popovski, Petar
    Sanguinetti, Luca
    de Carvalho, Elisabeth
    [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2022, 39 (02) : 135 - 158
  • [2] Foundations of MIMO Radar Detection Aided by Reconfigurable Intelligent Surfaces
    Buzzi, Stefano
    Grossi, Emanuele
    Lops, Marco
    Venturino, Luca
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2022, 70 : 1749 - 1763
  • [3] Non-Orthogonal Multiple Access (NOMA) With Multiple Intelligent Reflecting Surfaces
    Cheng, Yanyu
    Li, Kwok Hung
    Liu, Yuanwei
    Teh, Kah Chan
    Karagiannidis, George K.
    [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2021, 20 (11) : 7184 - 7195
  • [4] LOS/NLOS Near-Field Localization With a Large Reconfigurable Intelligent Surface
    Dardari, Davide
    Decarli, Nicolo
    Guerra, Anna
    Guidi, Francesco
    [J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2022, 21 (06) : 4282 - 4294
  • [5] Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come
    Di Renzo, Marco
    Debbah, Merouane
    Dinh-Thuy Phan-Huy
    Zappone, Alessio
    Alouini, Mohamed-Slim
    Yuen, Chau
    Sciancalepore, Vincenzo
    Alexandropoulos, George C.
    Hoydis, Jakob
    Gacanin, Haris
    de Rosny, Julien
    Bounceur, Ahcene
    Lerosey, Geoffroy
    Fink, Mathias
    [J]. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2019, 2019 (1)
  • [6] Optimal Choice of Weights for Sparse Recovery With Prior Information
    Flinth, Axel
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2016, 62 (07) : 4276 - 4284
  • [7] Recovering Compressively Sampled Signals Using Partial Support Information
    Friedlander, Michael P.
    Mansour, Hassan
    Saab, Rayan
    Yilmaz, Ozgur
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2012, 58 (02) : 1122 - 1134
  • [8] Localization and Channel Reconstruction for Extra Large RIS-Assisted Massive MIMO Systems
    Han, Yu
    Jin, Shi
    Wen, Chao-Kai
    Quek, Tony Q. S.
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2022, 16 (05) : 1011 - 1025
  • [9] Beyond 5G RIS mmWave Systems: Where Communication and Localization Meet
    He, Jiguang
    Jiang, Fan
    Keykhosravi, Kamran
    Kokkoniemi, Joonas
    Wymeersch, Henk
    Juntti, Markku
    [J]. IEEE ACCESS, 2022, 10 : 68075 - 68084
  • [10] Leveraging Ris-Enabled Smart Signal Propagation for Solving Infeasible Localization Problems
    Keykhosravi, Kamran
    Denis, Benoit
    Alexandropoulos, George C. C.
    He, Zhongxia Simon
    Albanese, Antonio
    Sciancalepore, Vincenzo
    Wymeersch, Henk
    [J]. IEEE VEHICULAR TECHNOLOGY MAGAZINE, 2023, 18 (02): : 20 - 28