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.
@article{arxiv.2306.00544,
title = {Codebook Configuration for RIS-aided Systems via Implicit Neural Representations},
author = {Huiying Yang and Rujing Xiong and Yao Xiao and Zhijie Fan and Tiebin Mi and Robert Caiming Qiu and Zenan Ling},
journal= {arXiv preprint arXiv:2306.00544},
year = {2024}
}