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Cell-Free Massive MIMO for ISAC: Access Point Operation Mode Selection and Power Control
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This paper considers a cell-free massive multipleinput multiple-output (MIMO) integrated sensing and communication (ISAC) system, where distributed MIMO access points (APs) are used to jointly serve the communication users and detect the presence of a single target. We investigate the problem of AP operation mode selection, wherein some APs are dedicated for downlink communication, while the remaining APs are used for sensing purposes. Closed-form expressions for the individual spectral efficiency (SE) and mainlobe-to-average-sidelobe ratio (MASR) are derived, which are respectively utilized to assess the communication and sensing performances. Accordingly, a maxmin fairness problem is formulated and solved, where the minimum SE of the users is maximized, subject to the per-AP power constraints as well as sensing MASR constraint. Our numerical results show that the proposed AP operation mode selection with power control can significantly improve the communication performance for given sensing requirements.
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Cited by 1 Pith paper
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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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