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.

翻译：可重构智能表面（RIS）被视作6G无线通信的关键使能技术。通过配置反射波束成形码本，RIS可将信号聚焦于目标接收器以增强信号强度。本文研究RIS辅助通信系统的码本配置问题。我们从信号辐射机制出发，建立了用户坐标信息与码本之间的隐式关系，并引入一种新颖的基于学习的方法——隐式神经表征（INRs）——来解决这种隐式的坐标到码本的映射问题。该方法仅需用户坐标信息，无需依赖信道模型。此外，针对1比特RIS的重要实际应用，我们将1比特码本配置建模为多标签分类问题，并提出一种1比特RIS的编码策略以降低码本维度，从而提升学习效率。仿真与实测数据的实验结果验证了本方法的显著优势。