Real-Time Handover in LEO Satellite Networks via Markov Chain-Guided Simulated Annealing

This paper presents a real-time handover and link assignment framework for low-Earth-orbit (LEO) satellite networks operating in dense urban canyons. The proposed Markov chain-guided simulated annealing (MCSA) algorithm optimizes user-to-satellite assignments under dynamic channel and capacity constraints. By incorporating Markov chains to guide state transitions, MCSA achieves faster convergence and more effective exploration than conventional simulated annealing. Simulations conducted in Ku-band urban canyon environments show that the framework achieves an average user satisfaction of about 97%, providing an approximately 10% improvement over genetic algorithm (GA) results. It also delivers 10–15% higher resource utilization, lower blocking rates comparable to integer linear programming (ILP), and superior runtime scalability with linear complexity O(k·|U|·|S|). These results confirm that MCSA provides a scalable and robust real-time mobility management solution for next-generation LEO satellite systems.