Beamforming for STAR-RIS-Aided Integrated Sensing and Communication Using Meta DRL
Eghbali, Yasoub; Faramarzi, Sajad; Taskou, Shiva Kazemi; Mili, Mohammad Robat; Rasti, Mehdi; Hossain, Ekram (2024-01-10)
IEEE
10.01.2024
Y. Eghbali, S. Faramarzi, S. K. Taskou, M. R. Mili, M. Rasti and E. Hossain, "Beamforming for STAR-RIS-Aided Integrated Sensing and Communication Using Meta DRL," in IEEE Wireless Communications Letters, vol. 13, no. 4, pp. 919-923, April 2024, doi: 10.1109/LWC.2024.3350446.
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202403152246
https://urn.fi/URN:NBN:fi:oulu-202403152246
Tiivistelmä
Abstract
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 actorcritic (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.
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 actorcritic (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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