Codebook Configuration for RIS-Aided Systems via Implicit Neural Representations

Reconfigurable Intelligent Surface (RIS) is envisioned to be an enabling technique in 6G wireless communications. By configuring the reflection beamforming codebook, RIS focuses signals on target receivers to enhance signal strength. In this paper, we investigate the codebook configuration for RIS-a...

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Bibliographic Details
Published inICC 2024 - IEEE International Conference on Communications pp. 1449 - 1454
Main Authors Yang, Huiying, Xiong, Rujing, Xiao, Yao, Fan, Zhijie, Mi, Tiebin, Qiu, Robert Caiming, Ling, Zenan
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.06.2024
Subjects
Encoding
Learning systems
Receivers
Reconfigurable intelligent surfaces
Reflection
Training
Wireless communication
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Summary:Reconfigurable Intelligent Surface (RIS) is envisioned to be an enabling technique in 6G wireless communications. By configuring the reflection beamforming codebook, RIS focuses signals on target receivers to enhance signal strength. In this paper, we investigate the codebook configuration for RIS-aided communication systems. We formulate an implicit relationship between user's coordinates information and the codebook from the perspective of signal radiation mechanisms, and introduce a novel learning-based method, implicit neural representations (INRs), to solve this implicit coordinates-to-codebook mapping problem. Our approach requires only user's coordinates, avoiding reliance on channel models. Additionally, given the significant practical applications of the 1-bit RIS, we formulate the 1-bit codebook configuration as a multi-label classification problem, and propose an encoding strategy for 1-bit RIS to reduce the codebook dimension, thereby improving learning efficiency. Experimental results from simulations and measured data demonstrate significant advantages of our method.
ISSN:1938-1883
DOI:10.1109/ICC51166.2024.10623051