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Electrical Engineering and Systems Science > Signal Processing

Title: Deep Cooperation in ISAC System: Resource, Node and Infrastructure Perspectives

Abstract: With the emerging Integrated Sensing and Communication (ISAC) technique, exploiting the mobile communication system with multi-domain resources, multiple network elements, and large-scale infrastructures to realize cooperative sensing is a crucial approach satisfying the requirements of high-accuracy and large-scale sensing in IoE. In this article, the deep cooperation in ISAC system including three perspectives is investigated. In the microscopic perspective, namely, within a single node, the sensing information carried by time-frequency-space-code domain resources is processed, such as phase compensation, coherent accumulation and other operations, thereby improving the sensing accuracy. In the mesoscopic perspective, the sensing accuracy could be improved through the cooperation of multiple nodes. We explore various multi-node cooperative sensing scenarios and present the corresponding challenges and future research trends. In the macroscopic perspective, the massive number of infrastructures from the same operator or different operators could perform cooperative sensing to extend the sensing coverage and improve the sensing continuity. We investigate network architecture, target tracking methods, and the large-scale sensing assisted digital twin construction. Simulation results demonstrate the superiority of multi-nodes and multi-resources cooperative sensing over single resource or node sensing. This article may provide a deep and comprehensive view on the cooperative sensing in ISAC system to enhance the performance of sensing, supporting the applications of IoE.
Comments: 8 pages and 6 figures, Accepted by IEEE Internet of Things Magazine
Subjects: Signal Processing (eess.SP)
Cite as: arXiv:2403.02565 [eess.SP]
  (or arXiv:2403.02565v2 [eess.SP] for this version)

Submission history

From: Haotian Liu [view email]
[v1] Tue, 5 Mar 2024 00:43:31 GMT (1176kb,D)
[v2] Thu, 30 May 2024 02:00:41 GMT (1227kb,D)

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