Journal
DIGITAL COMMUNICATIONS AND NETWORKS
Volume 8, Issue 2, Pages 87-104Publisher
KEAI PUBLISHING LTD
DOI: 10.1016/j.dcan.2021.11.002
Keywords
Reconfigurable intelligent surface; MIMO; Channel estimation; Beamforming; Reflectarray; Transmitarray; Machine learning; Deep learning
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This paper surveys the latest research outcomes in the field of Reconfigurable Intelligent Surface (RIS), which has the potential to significantly enhance the performance of next-generation wireless communication networks by adapting the propagation environment. The paper focuses on several aspects that affect the commercial viability of future network deployment. It provides comprehensive summaries of practical hardware design considerations, the broad implications of artificial intelligence techniques, as well as in-depth outlooks on system models, use cases, and physical layer optimization techniques.
In this paper, we survey state-of-the-art research outcomes in the burgeoning field of Reconfigurable Intelligent Surface (RIS), given its potential for significant performance enhancement of next-generation wireless communication networks by means of adapting a propagation environment. Emphasis has been placed on several aspects gating the commercial viability of future network deployment. Comprehensive summaries are provided for practical hardware design considerations and broad implications of artificial intelligence techniques, as are in-depth outlooks on the salient aspects of system models, use cases, and physical layer optimization techniques.
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