Adversarial bandit approach for RIS-aided OFDM communication


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Ahmed Ouameur, M., Anh, L. D. T., Massicotte, D., Jeon, G. et de Figueiredo, F. A. P. (2022). Adversarial bandit approach for RIS-aided OFDM communication. EURASIP Journal on Wireless Communications and Networking, 2022 (1). p. 111. ISSN 1687-1499 DOI 10.1186/s13638-022-02184-6

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To assist sixth-generation wireless systems in the management of a wide variety of services, ranging from mission-critical services to safety-critical tasks, key physical layer technologies such as reconfigurable intelligent surfaces (RISs) are proposed. Even though RISs are already used in various scenarios to enable the implementation of smart radio environments, they still face challenges with regard to real-time operation. Specifically, high dimensional fully passive RISs typically need costly system overhead for channel estimation. This paper, however, investigates a semi-passive RIS that requires a very low number of active elements, wherein only two pilots are required per channel coherence time. While in its infant stage, the application of deep learning (DL) tools shows promise in enabling feasible solutions. We propose two low-training overhead and energy-efficient adversarial bandit-based schemes with outstanding performance gains when compared to DL-based reflection beamforming reference methods. The resulting deep learning models are discussed using state-of-the-art model quality prediction trends.

Type de document: Article
Mots-clés libres: Reconfgurable intelligent surfaces Refection beamforming prediction Deep learning Machine learning Sixth-generation (6G) wireless systems Adversarial bandit Exponential-weight algorithm for exploration and exploitation Follow the perturbed leader (FPL)
Date de dépôt: 14 août 2023 15:35
Dernière modification: 14 août 2023 15:37
Version du document déposé: Version officielle de l'éditeur

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