标题： 基于隐式神经表示的RIS辅助系统码本配置 显示英文标题

标题： Codebook Configuration for RIS-aided Systems via Implicit Neural Representations

摘要： 可重构智能表面（RIS）被视为6G无线通信的一项关键技术。 通过配置反射波束成形码本，RIS能够将信号聚焦到目标接收器上以增强信号强度。 本文研究了RIS辅助通信系统的码本配置问题。 我们从信号辐射机制的角度建立了用户坐标信息与码本之间的隐式关系，并引入了一种基于学习的新方法——隐式神经表征（INRs），用于解决这一隐式的坐标到码本映射问题。 我们的方法只需要用户的坐标信息，避免了依赖信道模型。 此外，鉴于1比特RIS具有重要的实际应用价值，我们将1比特码本的配置问题建模为一个多标签分类问题，并提出了一种针对1比特RIS的编码策略，以降低码本维度，从而提高学习效率。 仿真和真实数据的实验结果证明了我们方法的显著优势。 显示英文摘要

摘要： 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.