In this letter, we propose a deep-unfolding-based framework (DUNet) to maximize the secrecy rate in reconfigurable intelligent surface (RIS) empowered multi-user wireless networks. To tailor DUNet, first we relax the problem, decouple it into beamforming and phase shift subproblems, and propose an alternative optimization (AO) based solution for the relaxed problem. Second, we apply Karush-Kuhn-Tucker (KKT) conditions to obtain a closed-form solutions for the beamforming and the phase shift. Using deep-unfolding mechanism, we transform the closed-form solutions into a deep learning model (i.e., DUNet) that achieves a comparable performance to that of AO in terms of accuracy and about 25.6 times faster.

翻译：本文提出一种基于深度展开的框架（DUNet），以最大化可重构智能表面（RIS）赋能多用户无线网络中的保密速率。为定制DUNet，首先对问题进行松弛，将其解耦为波束赋形与相位偏移两个子问题，并提出基于交替优化（AO）的松弛问题求解方案。其次，应用Karush-Kuhn-Tucker（KKT）条件推导波束赋形与相位偏移的闭式解。借助深度展开机制，将闭式解转化为深度学习模型（即DUNet），该模型在精度上达到与AO相当的性能，同时速度提升约25.6倍。