CHIMERA: Compressed Hybrid Intelligence for Twin-Model Enhanced Multi-Agent Deep Reinforcement Learning for Multi-Functional RIS-Assisted Space-Air-Ground Integrated Networks

2025-07-21

AI Summary

To address the energy scarcity of low-Earth-orbit (LEO) satellites in shadowed regions within Space-Air-Ground Integrated Networks (SAGIN), this paper investigates long-term energy efficiency (EE) maximization for joint communication and computation optimization assisted by multi-functional reconfigurable intelligent surfaces (MF-RIS). The problem is highly non-convex, nonlinear, and involves coupled mixed-integer-continuous variables. To tackle it, we propose CHIMERA—a multi-agent deep reinforcement learning framework leveraging semantic state-action compression and dual-model enhancement—to jointly optimize beamforming, RIS phase shifts, energy harvesting ratios, and high-altitude platform deployment. Compared with conventional RIS-assisted, RIS-free, and centralized RL baselines, CHIMERA achieves significant gains in long-term EE under complementary coverage scenarios. Results validate the effectiveness and superiority of semantic compression-driven distributed cooperative decision-making for EE optimization in SAGIN.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

A space-air-ground integrated network (SAGIN) architecture is proposed, empowered by multi-functional reconfigurable intelligent surfaces (MF-RIS) capable of simultaneously reflecting, amplifying, and harvesting wireless energy. The MF-RIS plays a pivotal role in addressing the energy shortages of low-Earth orbit (LEO) satellites operating in shadowed regions, while explicitly accounting for both communication and computing energy consumption across the SAGIN nodes. To maximize the long-term energy efficiency (EE), we formulate a joint optimization problem over the MF-RIS parameters, including signal amplification, phase-shifts, energy harvesting ratio, and active element selection as well as the SAGIN parameters of beamforming vectors, high-altitude platform station (HAPS) deployment, user association, and computing capability. The formulated problem is highly non-convex and non-linear and contains mixed discrete-continuous parameters. To tackle this, we conceive a compressed hybrid intelligence for twin-model enhanced multi-agent deep reinforcement learning (CHIMERA) framework, which integrates semantic state-action compression and parametrized sharing under hybrid reinforcement learning to efficiently explore suitable complex actions. The simulation results have demonstrated that the proposed CHIMERA scheme substantially outperforms the conventional benchmarks, including fixed-configuration or non-harvesting MF-RIS, traditional RIS, and no-RIS cases, as well as centralized and multi-agent deep reinforcement learning baselines in terms of the highest EE. Moreover, the proposed SAGIN-MF-RIS architecture achieves superior EE performance due to its complementary coverage, offering notable advantages over either standalone satellite, aerial, or ground-only deployments.

Problem

• Optimizing energy efficiency in multi-functional RIS-assisted SAGIN networks
• Addressing energy shortages for LEO satellites in shadowed regions
• Solving non-convex joint optimization of RIS and SAGIN parameters

Innovation

• MF-RIS for simultaneous energy and communication
• CHIMERA framework for hybrid reinforcement learning
• Joint optimization of SAGIN and MF-RIS parameters