Resilience Evaluation and Optimization Method of Large-Scale LEO Satellite Networks Based on Entropy Theory

low-Earth orbit (LEO) satellite networks, with their advantages of low latency, wide coverage, and high data rates, have become a core component of 6G networks. However, due to the complexity and interactivity of satellite networks, they face higher uncertainty and vulnerability, and there is currently a lack of effective frameworks for survivability and recovery in harsh environments. This article proposes a resilience evaluation and optimization model for large-scale LEO satellite networks, based on entropy theory and the minimum-cut theorem. Considering the time-varying characteristics of satellite networks, a spatiotemporal extended graph is used to model the network topology, along with a network communication model that incorporates node state transitions. By analyzing various factors, such as topological structure, node functionality, and link quality in real time, entropy theory is leveraged to provide a dynamic and comprehensive evaluation of LEO satellite network resilience. In response to network failures, multiple attributes, including node load and computational capacity, link signal-to-noise ratio (SNR), and link duration, are integrated into an optimization decision-making framework, formulating a resilient recovery optimization problem. To address this, a network resilience bottleneck identification and global load optimization-based edge augmentation strategy is proposed to enhance the network’s adaptive and rapid recovery capabilities. Simulation results demonstrate that the proposed resilience evaluation model effectively reflects the resilience performance of LEO satellite networks in complex and dynamic environments, and the proposed optimization method significantly enhances network resilience.