Movable intelligent surfaces boost energy efficient wireless networks
Movable-Element Multi-Functional RIS-Assisted Hybrid RSMA-NOMA: A Constrained Hybrid Reinforcement Learning Approach
Artificial Intelligence
Summary
Wireless networks need to send data efficiently while saving energy. The authors designed a special kind of smart surface that can move its parts and adjust signals in multiple ways to improve communication and power use. They combined two signal-sharing methods to make better use of the available spectrum. To figure out the best settings for sending data and controlling the smart surface, they used a new learning approach that handles complex decisions and shares knowledge between agents. Their tests show this setup can save energy and meet network constraints better than existing methods.
What this means in practice
- •For wireless network engineers: Optimize antenna and surface configurations to improve energy efficiency and signal quality in next-generation mobile networks.
- •For telecom infrastructure planners: Deploy movable-element intelligent surfaces to adaptively enhance network coverage and reduce energy consumption in wireless system infrastructures.
Authors
Li-Hsiang Shen, Chi-Te Kuo
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.