Empowering ISAC Systems With Federated Learning: A Focus on Satellite and RIS-Enhanced Terrestrial Integrated Networks

This paper presents a state-of-the-art analytical framework aimed to enhance spectral efficiency in satellite and terrestrial integrated networks (STINs), utilizing reconfigurable intelligent surface (RIS) within the realm of integrated sensing and communication (ISAC). Our methodology pivots on a pioneering federated deep reinforcement learning strategy that introduces new ground beyond conventional optimization techniques to tackle the intricate problem of non-convex resource allocation. The approach leverages federated learning to dynamically adapt to network changes, enabling efficient resource management and ensuring compliance with beamforming designs, multiple target signal-to-interference-plus-noise ratio thresholds, and RIS phase-shift requirements through an effective feedback loop. In particular, we propose a federated deep deterministic policy gradient (F-DDPG) algorithm across multi-agent systems that outperforms existing federated deep Q-network (F-DQN), centralized, and traditional DDPG and DQN methods. The empirical findings underscore the efficiency of the federated algorithms, which closely align with the performance of centralized models while markedly reducing execution time, thus achieving an optimal synergy between operational efficiency and system performance. Simulation results highlight the remarkable advantages of optimal RIS configurations, showcasing a performance increase of 54.2% over random RIS setups and a remarkable 76.8% enhancement compared to scenarios without RIS, underscoring the transformative impact of our federated learning approach. Additionally, our study evaluates the impact of channel estimation errors and interference, confirming the robustness of our approach and its potential to optimize ISAC-enabled STINs.