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Networked Integrated Sensing and Communications for 6G Wireless Systems

  • Jiapeng Li
  • , Xiaodan Shao
  • , Feng Chen
  • , Shaohua Wan
  • , Chang Liu
  • , Zhiqiang Wei
  • , Derrick Wing Kwan Ng
  • Southwest University
  • University of Electronic Science and Technology of China
  • La Trobe University
  • Peng Cheng Laboratory
  • Guangdong Artificial Intelligence and Digital Economy Laboratory - Guangzhou
  • University of New South Wales

科研成果: 期刊稿件文章同行评审

29 引用 (Scopus)

摘要

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this article, we design a novel networked ISAC framework to explore the collaboration among multiple users for environmental sensing. Specifically, multiple users can serve as powerful sensors, capturing back scattered signals from a target at various angles to facilitate reliable computational imaging. Centralized sensing approaches are extremely sensitive to the capability of the leader node because it requires the leader node to process the signals sent by all the users. To this end, we propose a two-step distributed cooperative sensing algorithm that allows low-dimensional intermediate estimate exchange among the neighboring users, thus eliminating the reliance on the centralized leader node and improving the robustness of sensing. This way, multiple users can cooperatively sense a target by exploiting the block-wise environment sparsity and the interference cancelation technique. Furthermore, we analyse the mean-square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint. Simulation results validate the effectiveness of the proposed algorithm compared with the state-of-the-art algorithms.

源语言英语
页(从-至)29062-29075
页数14
期刊IEEE Internet of Things Journal
11
17
DOI
出版状态已出版 - 2024

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