LLM-guided DRL for Multi-tier LEO Satellite Networks with Hybrid FSO/RF Links

TL;DR

This paper introduces a novel LLM-guided reinforcement learning algorithm for optimizing a three-tier LEO satellite network with hybrid FSO/RF links, improving coverage reliability and reducing handovers.

Contribution

It proposes a new LLM-guided RL algorithm with dynamic action masking for efficient network configuration and satellite handover optimization in multi-tier satellite networks.

Findings

The LTQC-DAM algorithm outperforms baseline methods in convergence speed.

It achieves higher downlink transmission rates.

It reduces handover frequency effectively.

Abstract

Despite significant advancements in terrestrial networks, inherent limitations persist in providing reliable coverage to remote areas and maintaining resilience during natural disasters. Multi-tier networks with low Earth orbit (LEO) satellites and high-altitude platforms (HAPs) offer promising solutions, but face challenges from high mobility and dynamic channel conditions that cause unstable connections and frequent handovers. In this paper, we design a three-tier network architecture that integrates LEO satellites, HAPs, and ground terminals with hybrid free-space optical (FSO) and radio frequency (RF) links to maximize coverage while maintaining connectivity reliability. This hybrid approach leverages the high bandwidth of FSO for satellite-to-HAP links and the weather resilience of RF for HAP-to-ground links. We formulate a joint optimization problem to simultaneously balance downlink transmission rate and handover frequency by optimizing network configuration and satellite handover decisions. The problem is highly dynamic and non-convex with time-coupled constraints. To address these challenges, we propose a novel large language model (LLM)-guided truncated quantile critics algorithm with dynamic action masking (LTQC-DAM) that utilizes dynamic action masking to eliminate unnecessary exploration and employs LLMs to adaptively tune hyperparameters. Simulation results demonstrate that the proposed LTQC-DAM algorithm outperforms baseline algorithms in terms of convergence, downlink transmission rate, and handover frequency. We also reveal that compared to other state-of-the-art LLMs, DeepSeek delivers the best performance through gradual, contextually-aware parameter adjustments.