Adversarial bandit approach for RIS-aided OFDM communication.

Ahmed Ouameur, Messaoud; Anh, Lê DÆ°Æ¡ng Tuan; Massicotte, Daniel; Jeon, Gwanggil; de Figueiredo, Felipe Augusto Pereira

Ahmed Ouameur, Messaoud; Anh, Lê DÆ°Æ¡ng Tuan; Massicotte, Daniel; Jeon, Gwanggil; de Figueiredo, Felipe Augusto Pereira.

Afiliação

Ahmed Ouameur M; Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, 3351 Boul des Forges, Trois-Rivières, QC G9A 5H7 Canada.
Anh LDT; Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, 3351 Boul des Forges, Trois-Rivières, QC G9A 5H7 Canada.
Massicotte D; Faculty of Information Technology, VNU-HCM University of Science, Ho Chi Minh City, Vietnam.
Jeon G; Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, 3351 Boul des Forges, Trois-Rivières, QC G9A 5H7 Canada.
de Figueiredo FAP; Department of Embedded Systems Engineering, College of Information Technology, Incheon National University, Incheon, South Korea.

EURASIP J Wirel Commun Netw ; 2022(1): 111, 2022.

Article em En | MEDLINE | ID: mdl-36411764

Palavras-chave

Adversarial bandit; Deep learning; Exponential-weight algorithm for exploration and exploitation; Follow the perturbed leader (FPL); Machine learning; Reconfigurable intelligent surfaces; Reflection beamforming prediction; Sixth-generation (6G) wireless systems

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: EURASIP J Wirel Commun Netw Ano de publicação: 2022 Tipo de documento: Article

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