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- W3161082425 abstract "The reconfigurable intelligent surface (RIS) technology has attracted interest due to its promising coverage and spectral efficiency features. However, some challenges need to be addressed to realize this technology in practice. One of the main challenges is the configuration of reflecting coefficients without the need for beam training overhead or massive channel estimation. Earlier works used estimated channel information with deep learning algorithms to design RIS reflection matrices. Although these works can reduce the beam training overhead, still they overlook existing correlations in the previously sampled channels. In this paper, different from existing works, we propose to exploit the correlation in the previously sampled channels to estimate RIS interaction more reliably. We use a deep multilayer perceptron for this purpose. Simulation results reveal performance improvements achieved by the proposed algorithm." @default.
- W3161082425 created "2021-05-24" @default.
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- W3161082425 date "2021-03-29" @default.
- W3161082425 modified "2023-10-06" @default.
- W3161082425 title "Deep Learning-Based Optimal RIS Interaction Exploiting Previously Sampled Channel Correlations" @default.
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- W3161082425 doi "https://doi.org/10.1109/wcnc49053.2021.9417591" @default.
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