The development of 6G/B5G wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academic and industrial. However, to increase 6G/B5G network quality, conventional cellular networks that rely on terrestrial base stations are constrained geographically and economically. Meanwhile, NOMA allows multiple users to share the same resources, which improves the spectral efficiency of the system and has the advantage of supporting a larger number of users. Additionally, by intelligently manipulating the phase and amplitude of both the reflected and transmitted signals, STAR-RISs can achieve improved coverage, increased spectral efficiency, and enhanced communication reliability. However, STAR-RISs must simultaneously optimize the Amplitude and Phase-shift corresponding to reflection and transmission, which makes the existing terrestiral networks more complicated and is considered a major challenging issue. Motivated by the above, we study the joint user pairing for NOMA and beamforming design of Multi-STAR-RISs in an indoor environment. Then, we formulate the optimization problem with the objective of maximizing the total throughput of MUs by jointly optimizing the decoding order, user pairing, active beamforming, and passive beamforming. However, the formulated problem is a MINLP. To tackle this challenge, we first introduce the decoding order for NOMA networks. Next, we decompose the original problem into two subproblems namely: 1) MU pairing and 2) Beamforming optimization under the optimal decoding order. For the first subproblem, we employ correlation-based K-means clustering to solve the user pairing problem. Then, to jointly deal with beamforming vector optimizations, we propose MAPPO, which can make quick decisions in the given environment owing to its low complexity.

翻译：6G/B5G无线网络的发展正引起学术界和工业界的广泛关注，其性能要求已超越现有5G网络。然而，为提升6G/B5G网络质量，传统依赖地面基站的蜂窝网络受限于地理和经济因素。同时，非正交多址接入（NOMA）允许多用户共享相同资源，不仅提高了系统频谱效率，还具备支持更多用户的优势。此外，通过智能调控反射和透射信号的幅度与相位，同时透射和反射可重构智能表面（STAR-RISs）能够实现覆盖增强、频谱效率提升及通信可靠性改善。但STAR-RIS需同时优化反射和透射对应的幅度与相移，这使现有地面网络更加复杂，成为重大挑战。基于此，本文研究了室内环境下多STAR-RIS辅助NOMA系统的联合用户配对与波束赋形设计问题。通过联合优化解码顺序、用户配对、主动波束赋形和被动波束赋形，建立以最大化用户总吞吐量为目标的优化问题。该问题为混合整数非线性规划（MINLP）。为应对这一挑战，我们首先引入NOMA网络解码顺序，进而将原问题分解为两个子问题：1）最优解码顺序下的用户配对；2）波束赋形优化。针对第一个子问题，采用基于相关性的K-means聚类算法解决用户配对问题。随后，为联合处理波束赋形向量优化，提出多智能体近端策略优化（MAPPO）算法，该算法凭借低复杂度特性可在给定环境中快速决策。