Beam Hopping Low Earth Orbit Satellite Resource Allocation for Differentiated Services and Robustness Analysis under Model Attacks

2026-07-04

AI Summary

This work addresses the challenge of delivering efficient and robust differentiated quality-of-service (QoS) from low Earth orbit (LEO) satellites under stringent onboard resource constraints and adversarial perturbations. To this end, the authors propose BRIDGE, a novel framework that leverages digital twin technology to accurately model user–satellite spatiotemporal visibility and formulates a joint optimization of beam hopping and power allocation. By integrating a Dirichlet prior with a Gumbel-TopK exploration mechanism, BRIDGE enables dynamic, QoS-driven resource scheduling. Experimental results demonstrate that the method significantly improves energy efficiency, throughput, and fairness, while maintaining stable performance under three representative adversarial attack scenarios—effectively balancing service differentiation and robustness.

Abstract

Beam hopping (BH)-enabled Low Earth Orbit (LEO) satellites play a pivotal role in next-generation communication networks by providing global coverage, improving spectrum efficiency, and supporting flexible adaptation to heterogeneous service demands. To fully exploit these capabilities, artificial intelligence (AI) techniques are increasingly employed for dynamic resource allocation and power management. However, limited onboard resources and potential adversarial perturbations pose challenges to both efficiency and robustness. To address these issues, we leverage digital twin technology to accurately capture the spatio-temporal dynamics of user-satellite visibility, thereby providing precise state information for decision-making. Building on this, we formulate a joint optimization framework for BH scheduling and power allocation as a Markov Decision Process and propose BRIDGE, i.e., BH with Reinforcement learning incorporating Integrated Dirichlet and Gumbel-TopK Exploration, which integrates a quality of service (QoS)-driven subchannel scheduling mechanism to ensure efficient and differentiated resource allocation. The robustness of the model is systematically evaluated under three classical adversarial attacks. Simulation results demonstrate that the proposed approach achieves superior energy efficiency, service throughput, and fairness, while the robustness analysis shows stable performance under the considered bounded adversarial perturbations.

Problem

• Beam Hopping
• LEO Satellite
• Resource Allocation
• Differentiated Services
• Robustness

Innovation

• Beam Hopping
• Digital Twin
• Reinforcement Learning
• Adversarial Robustness
• LEO Satellite