Operating Reconfigurable Intelligent Surfaces (RIS) requires a control strategy capable of coping with a constantly changing propagation environment. The use of a memory-enhanced evolutionary strategy is proposed here to exploit good solutions found at previous times and boost the exploration of the highly-dimensional solution space. The proposed approach does not require channel sensing at the RIS and its potentiality is demonstrated in a small-scale numerical analysis. I
Memory-Enhanced Evolutionary Strategy for QoS-Driven RIS Control / Zardi, F.; Oliveri, G.; Salucci, M.; Massa, A.. - STAMPA. - 2023-July:(2023), pp. 987-988. ( 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 Portland, OR, USA 23th July -28th July 2023) [10.1109/usnc-ursi52151.2023.10238118].
Memory-Enhanced Evolutionary Strategy for QoS-Driven RIS Control
Zardi, F.;Oliveri, G.;Salucci, M.;Massa, A.
2023-01-01
Abstract
Operating Reconfigurable Intelligent Surfaces (RIS) requires a control strategy capable of coping with a constantly changing propagation environment. The use of a memory-enhanced evolutionary strategy is proposed here to exploit good solutions found at previous times and boost the exploration of the highly-dimensional solution space. The proposed approach does not require channel sensing at the RIS and its potentiality is demonstrated in a small-scale numerical analysis. I| File | Dimensione | Formato | |
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C597.pdf
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Descrizione: Antennas and Propagation & USNC/URSI National Radio Science Meeting - conference article
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