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Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks

arXiv 2605.02416 ↗First posted 4 May 2026cs.IT cs.LG math.ITcited by 0

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Abstract (arXiv)

In this paper, we propose a dueling double deep Q-network (DDQN)-based adaptive multi-objective handover framework for low Earth orbit (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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 22 references that are in the map. First posted on arXiv 4 May 2026.

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References · 6

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  3. 2024Non-Terrestrial Networks for 6G: Integrated, Intelligent and Ubiquitous Connectivitycited by 5
  4. 2025DRL-Based Beam Positioning for LEO Satellite Constellations with Weighted Least Squarescited by 2
  5. 2022Continent-wide Efficient and Fair Downlink Resource Allocation in LEO Satellite Constellationscited by 1
  6. 2022A Graph-Based Customizable Handover Framework for LEO Satellite Networkscited by 1

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