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arXiv 2306.00544cs.ITeess.SPmath.IT
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基于隐式神经表示的可重构智能表面辅助系统的码本配置

Codebook Configuration for RIS-aided Systems via Implicit Neural Representations

Huiying Yang, Rujing Xiong, Yao Xiao, Zhijie Fan, Tiebin Mi, Robert Caiming Qiu, Zenan Ling

AI总结:

本文提出利用隐式神经表示学习用户坐标到RIS码本的映射,仅需坐标信息,无需信道模型,并针对1比特RIS提出编码策略降低维度,仿真和实测验证了方法的有效性。

摘要:

可重构智能表面(RIS)被视为6G无线通信中的一项使能技术。通过配置反射波束成形码本,RIS将信号聚焦到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,并引入一种新颖的基于学习的方法——隐式神经表示(INRs)来解决这一坐标到码本的隐式映射问题。我们的方法仅需用户坐标,避免了对信道模型的依赖。此外,鉴于1比特RIS的重要实际应用,我们将1比特码本配置问题表述为多标签分类问题,并提出一种针对1比特RIS的编码策略以降低码本维度,从而提高学习效率。仿真和实测数据的实验结果表明了我们的方法的显著优势。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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