TY - GEN
T1 - DEEP-LEARNING-ASSISTED CONFIGURATION OF RECONFIGURABLE INTELLIGENT SURFACES IN DYNAMIC RICH-SCATTERING ENVIRONMENTS
AU - Stylianopoulos, Kyriakos
AU - Shlezinger, Nir
AU - del Hougne, Philipp
AU - Alexandropoulos, George C.
N1 - Publisher Copyright:
© 2022 IEEE
PY - 2022/1/1
Y1 - 2022/1/1
N2 - The integration of Reconfigurable Intelligent Surfaces (RISs) into wireless environments endows channels with programmability, and is expected to play a key role in future communication standards. To date, most RIS-related efforts focus on quasi-free-space, where wireless channels are typically modeled analytically. Many realistic communication scenarios occur, however, in rich-scattering environments which, moreover, evolve dynamically. These conditions present a tremendous challenge in identifying an RIS configuration that optimizes the achievable communication rate. In this paper, we make a first step toward tackling this challenge. Based on a simulator that is faithful to the underlying wave physics, we train a deep neural network as surrogate forward model to capture the stochastic dependence of wireless channels on the RIS configuration under dynamic rich-scattering conditions. Subsequently, we use this model in combination with a genetic algorithm to identify RIS configurations optimizing the communication rate. We numerically demonstrate the ability of the proposed approach to tune RISs to improve the achievable rate in rich-scattering setups.
AB - The integration of Reconfigurable Intelligent Surfaces (RISs) into wireless environments endows channels with programmability, and is expected to play a key role in future communication standards. To date, most RIS-related efforts focus on quasi-free-space, where wireless channels are typically modeled analytically. Many realistic communication scenarios occur, however, in rich-scattering environments which, moreover, evolve dynamically. These conditions present a tremendous challenge in identifying an RIS configuration that optimizes the achievable communication rate. In this paper, we make a first step toward tackling this challenge. Based on a simulator that is faithful to the underlying wave physics, we train a deep neural network as surrogate forward model to capture the stochastic dependence of wireless channels on the RIS configuration under dynamic rich-scattering conditions. Subsequently, we use this model in combination with a genetic algorithm to identify RIS configurations optimizing the communication rate. We numerically demonstrate the ability of the proposed approach to tune RISs to improve the achievable rate in rich-scattering setups.
KW - deep learning
KW - dynamic wireless environments
KW - Reconfigurable intelligent surfaces
KW - rich-scattering
UR - http://www.scopus.com/inward/record.url?scp=85125806348&partnerID=8YFLogxK
U2 - 10.1109/ICASSP43922.2022.9746311
DO - 10.1109/ICASSP43922.2022.9746311
M3 - Conference contribution
AN - SCOPUS:85125806348
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 8822
EP - 8826
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -