Adaptive Personalized Federated Reinforcement Learning for RIS-Assisted Aerial Relays in SAGINs with Fluid Antennas

TL;DR

This paper proposes an adaptive personalized federated reinforcement learning approach to optimize UAV trajectories and RIS phase controls in a complex SAGIN environment, improving wireless service quality amidst environmental dynamics.

Contribution

It introduces a novel federated reinforcement learning framework tailored for dynamic SAGINs with RIS and fluid antennas, addressing heterogeneity and mobility challenges.

Findings

The proposed FRL algorithm outperforms baseline methods in simulation.

Personalization significantly enhances adaptation to environmental changes.

The system effectively manages multi-hotspot rate maximization in dynamic scenarios.

Abstract

Space-air-ground integrated networks (SAGINs) interconnect satellites, uncrewed aerial vehicles (UAVs), and ground devices to enable flexible and ubiquitous wireless services. The integration of reconfigurable intelligent surfaces (RISs) and fluid antenna systems (FASs) further enhances radio environment controllability. However, the tight integration of cross-layer facilities and radio enhancement technologies leads to pronounced environmental dynamics and heterogeneity, posing fundamental challenges for system modeling and optimization in large-scale SAGINs. This paper investigates a SAGIN in which low Earth orbit (LEO) satellite constellations communicate with multiple ground hotspots via RIS-assisted UAV relays, serving both FAS-equipped and conventional users. A system model is developed that explicitly captures satellite mobility, UAV trajectories, RIS phase control, and heterogeneous user reception capabilities. Accordingly, a multi-hotspot downlink rate maximization problem is studied, whose solvability is analyzed through a hierarchical Stackelberg game. To address heterogeneous and time-varying multi-hotspot environments, an adaptive personalized federated reinforcement learning (FRL) algorithm is proposed for adaptive optimization of UAV trajectories and RIS phase controls. Simulation results demonstrate superior performance and validate the effectiveness of personalization in dynamic heterogeneous SAGIN scenarios.