arXiv cs.AI· Li-Hsiang Shen, Chi-Te Kuo·· 12 小时前AI 评分18
MM-RIS：可移动单元多功能 RIS 辅助混合 RSMA-NOMA 的约束混合强化学习方法
Movable-Element Multi-Functional RIS-Assisted Hybrid RSMA-NOMA: A Constrained Hybrid Reinforcement Learning Approach
AI 导读
研究提出 MM-RIS 架构，将可移动单元与自适应反射、透射、放大及能量收集集成，并设计混合 RSMA-NOMA 传输方案以提升频谱效率。为最大化能量效率，作者提出 CRAFTS 框架，用双向参数共享结合 MAPPO 与 MADQN 分别处理连续和离散动作，并引入 Transformer 架构、联邦学习与基于拉格朗日的自适应惩罚机制。仿真显示该架构与框架在能量效率和约束满足上均优于现有基准。
正文
Abstract:In this work, we propose a movable-element (ME) multi-functional reconfigurable intelligent surface (MF-RIS) architecture, termed MM-RIS, which integrates element mobility with adaptive signal reflection, transmission, amplification, and energy harvesting (EH) to enhance communication flexibility and energy self-sustainability. A hybrid rate-splitting multiple access (RSMA) and non-orthogonal multiple access (NOMA) transmission scheme is further designed to improve spectral efficiency. We formulate an energy efficiency (EE) maximization problem by jointly optimizing transmit beamforming, power and rate allocation, MM-RIS configurations, panel deployment, and ME positions. To address the resulting non-convex mixed continuous-discrete optimization problem, we propose constrained hybrid deep reinforcement learning with federated transformer and parametrized sharing (CRAFTS). CRAFTS integrates multi-agent proximal policy optimization (MAPPO) and multi-agent deep Q-networks (MADQN) through bi-directional parametrized sharing to handle continuous and discrete actions, respectively. Moreover, a transformer-based architecture and federated learning (FL) are incorporated to enhance state representation and distributed knowledge sharing, whilst a Lagrangian-based adaptive penalty mechanism facilitates the constraint satisfaction. Simulation results have demonstrated that the proposed MM-RIS architecture and CRAFTS framework substantially improve EE and constraint satisfaction compared to the benchmarks in the open literature. Moreover, the results validate the benefits of spatially reconfigurable MM-RIS, hybrid RSMA-NOMA transmissions, and intelligent collaborative learning, highlighting the potential of the proposed framework for energy-efficient and adaptive next-generation wireless networks.
| Comments: | Accepted by IEEE TCOM |
| Subjects: | Artificial Intelligence (cs.AI); Signal Processing (eess.SP) |
| Cite as: | arXiv:2610.34701 [cs.AI] |
| (or arXiv:2610.34701v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34701 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Li-Hsiang Shen [view email]
[v1]
Mon, 26 Oct 2026 01:46:27 UTC (7,345 KB)
来源：arXiv cs.AI · arxiv.org