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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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Published in: | IEEE wireless communications letters 2024-04, Vol.13 (4), p.919-923 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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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. |
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ISSN: | 2162-2337 2162-2345 |
DOI: | 10.1109/LWC.2024.3350446 |