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Cooperative Cell-Free ISAC Networks: Joint BS Mode Selection and Beamforming Design

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arxiv 2305.10800 v3 pith:LQHZDMEU submitted 2023-05-18 cs.IT eess.SPmath.IT

classification cs.ITeess.SPmath.IT
keywords isacnetworksselectioncooperativemodebeamformingjointproblem
verification ladder T0 review T1 audit T2 compute T3 formal
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Owing to the promising ability of saving hardware cost and spectrum resources, integrated sensing and communication (ISAC) is regarded as a revolutionary technology for future sixth-generation (6G) networks. The mono-static ISAC systems considered in most of existing works can only achieve limited sensing performance due to the single observation angle and easily blocked transmission links, which motivates researchers to investigate cooperative ISAC networks. In order to further improve the degrees of freedom (DoFs) of cooperative ISAC networks, the transmitter-receiver selection, i.e., base station (BS) mode selection problem, is meaningful to be studied. However, to our best knowledge, this crucial problem has not been extensively studied in existing works. In this paper, we consider the joint BS mode selection, transmit beamforming, and receive filter designs for cooperative cell-free ISAC networks, where multi-BSs cooperatively serve communication users and detect targets. An efficient joint beamforming design algorithm and three different heuristic BS mode selection methods are proposed to solve the non-convex NP-hard problem. Simulation results demonstrates the advantages of cooperative ISAC networks, the importance of BS mode selection, and the effectiveness of proposed algorithms.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Two-Stage Distributed Beamforming Design in Cell-Free Massive MIMO ISAC Systems

    eess.SP 2025-01 conditional novelty 6.0 of 10

    A two-stage split of beamforming in cell-free ISAC reduces fronthaul exchange to 6NiterMK scalars, independent of antenna count, with simulated performance close to a centralized solution.

  2. Unsupervised Learning Approach for Beamforming in Cell-Free Integrated Sensing and Communication

    eess.SP 2024-12 conditional novelty 6.0 of 10

    An unsupervised teacher-student deep learning approach jointly designs communication and sensing beamformers for cell-free ISAC, achieving near-CVX performance with a reported three-order-of-magnitude runtime reduction.

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