RIS Phase Optimization via Generative Flow Networks
Chaaya, Charbel Bou; Bennis, Mehdi (2024-05-10)
IEEE
10.05.2024
C. Bou Chaaya and M. Bennis, "RIS Phase Optimization via Generative Flow Networks," in IEEE Wireless Communications Letters, vol. 13, no. 7, pp. 1988-1992, July 2024, doi: 10.1109/LWC.2024.3400127.
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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-202406244876
https://urn.fi/URN:NBN:fi:oulu-202406244876
Tiivistelmä
Abstract
This letter introduces a new Machine Learning (ML) technique to learn phase shifting patterns for Reconfigurable Intelligent Surfaces (RISs). We leverage the Generative Flow Network (GFlowNet) paradigm and adapt it so as to compose a RIS phase control resulting in high communication rate. To generalize our approach for different physical layer scenarios, we use a channel chart as a latent representation of the wireless spatial environment to condition the GFlowNet. As such, the GFlowNet learns a scalable policy over RIS configurations that tailors the propagation environment in real-time. We evaluate our solution by means of simulations on a synthetic dataset, and the results corroborate its superiority compared to benchmarks, achieving more than 15% higher communication rates.
This letter introduces a new Machine Learning (ML) technique to learn phase shifting patterns for Reconfigurable Intelligent Surfaces (RISs). We leverage the Generative Flow Network (GFlowNet) paradigm and adapt it so as to compose a RIS phase control resulting in high communication rate. To generalize our approach for different physical layer scenarios, we use a channel chart as a latent representation of the wireless spatial environment to condition the GFlowNet. As such, the GFlowNet learns a scalable policy over RIS configurations that tailors the propagation environment in real-time. We evaluate our solution by means of simulations on a synthetic dataset, and the results corroborate its superiority compared to benchmarks, achieving more than 15% higher communication rates.
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