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arXiv 2403.04028cs.ITeess.SPmath.IT
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RISnet:一种面向存在互耦与部分CSI的RIS优化的领域知识驱动神经网络架构

RISnet: A Domain-Knowledge Driven Neural Network Architecture for RIS Optimization with Mutual Coupling and Partial CSI

Bile Peng, Karl-Ludwig Besser, Shanpu Shen, Finn Siegismund-Poschmann, Ramprasad Raghunath, Daniel Mittleman, Vahid Jamali, Eduard A. Jorswieck

AI总结:

针对RIS优化中互耦、大尺度可扩展性及信道估计要求高的问题,提出结合领域知识的无监督神经网络架构RISnet,联合优化基站SDMA预编码与RIS配置,性能与复杂度优于通用ML方案。

摘要:

空分多址(SDMA)在现代无线通信中发挥着重要作用,其性能取决于信道特性,而可重构智能表面(RISs)可改善信道特性。本研究联合优化基站(BS)端的SDMA预编码与RIS配置,解决了RIS单元间互耦、超1000个RIS单元的可扩展性,以及信道估计要求高的难题。我们首先推导了考虑互耦的RIS辅助信道模型,随后提出一种无监督机器学习(ML)方法,采用专用神经网络(NN)架构RISnet优化RIS,该架构具备良好的可扩展性、理想的排列不变性,且对信道估计的要求较低。此外,我们结合现有高性能解析预编码方案,提出了ML驱动RIS配置与基站端解析预编码的混合解决方案。更广泛而言,本工作是通信领域将ML技术与领域知识结合用于神经网络架构设计的早期成果。与通用ML相比,面向特定问题的ML可实现更高性能、更低复杂度并具备排列不变性。

英文摘要:

Space-division multiple access (SDMA) plays an important role in modern wireless communications. Its performance depends on the channel properties, which can be improved by reconfigurable intelligent surfaces (RISs). In this work, we jointly optimize SDMA precoding at the base station (BS) and RIS configuration. We tackle difficulties of mutual coupling between RIS elements, scalability to more than 1000 RIS elements, and high requirement for channel estimation. We first derive an RIS-assisted channel model considering mutual coupling, then propose an unsupervised machine learning (ML) approach to optimize the RIS with a dedicated neural network (NN) architecture RISnet, which has good scalability, desired permutation-invariance, and a low requirement for channel estimation. Moreover, we leverage existing high-performance analytical precoding scheme to propose a hybrid solution of ML-enabled RIS configuration and analytical precoding at BS. More generally, this work is an early contribution to combine ML technique and domain knowledge in communication for NN architecture design. Compared to generic ML, the problem-specific ML can achieve higher performance, lower complexity and permutation-invariance.

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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