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Beamforming for STAR-RIS-Aided Integrated Sensing and Communication Using Meta DRL

We consider an integrated sensing and communication (ISAC) system, in which a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assists a base station in transmitting communication signals to mobile users and conducting sensing tasks toward specific targets. We...

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Bibliographic Details
Published in:IEEE wireless communications letters 2024-04, Vol.13 (4), p.919-923
Main Authors: Eghbali, Yasoub, Faramarzi, Sajad, Taskou, Shiva Kazemi, Mili, Mohammad Robat, Rasti, Mehdi, Hossain, Ekram
Format: Article
Language:English
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Summary:We consider an integrated sensing and communication (ISAC) system, in which a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assists a base station in transmitting communication signals to mobile users and conducting sensing tasks toward specific targets. We formulate a transmit beamforming and phase shift optimization problem to jointly maximize the total communication data rate and total effective sensing power. The optimization problem is inherently non-convex, making it challenging to find an optimal solution. To tackle this difficulty, we propose a meta soft actor-critic (meta-SAC) algorithm, which is a fusion of the SAC algorithm and meta-learning techniques. Through extensive simulations, we demonstrate that the proposed meta-SAC algorithm outperforms traditional deep reinforcement learning methods, thus showing its potential to enhance the performance of ISAC systems significantly.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2024.3350446