Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks
Po-Heng Chou, Chiapin Wang, Chung-Chi Huang, Kuan-Hao Chen
cs.IT cs.LG
Abstract
In this paper, we propose a dueling double deep Q-network (DDQN)-based adaptive multi-objective handover framework for LEO satellite networks. The proposed method enables dynamic trade-off learning among throughput, blocking probability, and switching cost under time-varying network conditions. Simulation results demonstrate that the proposed approach consistently outperforms conventional baselines, achieving up to 10.3% throughput improvement and near-zero blocking under typical operating conditions.
Topics
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