Reconfigurable intelligent surfaces (RISs) have become one of the key
technologies in 6G wireless communications. By configuring the reflection
beamforming codebooks, RIS focuses signals on target receivers. In this paper,
we investigate the codebook configuration for 1-bit RIS-aided systems. We
propose a novel learning-based method built upon the advanced methodology of
implicit neural representations. The proposed model learns a continuous and
differentiable coordinate-to-codebook representation from samplings. Our method
only requires the information of the user's coordinate and avoids the
assumption of channel models. Moreover, we propose an encoding-decoding
strategy to reduce the dimension of codebooks, and thus improve the learning
efficiency of the proposed method. Experimental results on simulation and
measured data demonstrated the remarkable advantages of the proposed method