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Cell-Free ISAC MIMO Systems: Joint Sensing and Communication Beamforming
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This paper considers a cell-free integrated sensing and communication (ISAC) MIMO system, where distributed MIMO access points (APs) jointly serve the communication users and sense the target. For this setup, we derive a sensing SNR for multi-static sensing where both joint communication and sensing signals transmitted by different APs are utilized. With this sensing objective, we develop two baseline approaches that separately design the sensing and communication beamforming vectors, namely communication-prioritized sensing beamforming and sensing-prioritized communication beamforming. Then, we consider the joint sensing and communication (JSC) beamforming design and derive the optimal structure of these beamforming vectors based on a max-min fairness formulation. In addition, considering any pre-determined JSC beam design, we devise a power allocation approach. The results show that the developed JSC beamforming is capable of achieving nearly the same communication signal-to-interference-plus-noise ratio (SINR) of the communication-prioritized sensing beamforming solution with almost the same sensing SNR of the sensing-prioritized communication beamforming approach. The proposed JSC beamforming optimization also provides a noticeable gain over the power allocation with regularized zero-forcing beamforming, yielding a promising strategy for cell-free ISAC MIMO systems.
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Cited by 2 Pith papers
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Unsupervised Learning Approach for Beamforming in Cell-Free Integrated Sensing and Communication
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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Federated Learning Strategies for Coordinated Beamforming in Multicell ISAC
Two federated learning frameworks for multicell ISAC beamforming are proposed: a VFL approach with a central server and an HFL approach with a fully distributed leakage-penalty loss function.
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