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

arXiv: https://arxiv.org/abs/2605.02416 · HTML: https://mlsanity.com/paper/2605.02416/

## 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.

## Builds on (most cited first, 6 of 6)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Non-Terrestrial Networks for 6G: Integrated, Intelligent and Ubiquitous Connectivity](https://mlsanity.com/paper/2407.02184/) (2024)
- [DRL-Based Beam Positioning for LEO Satellite Constellations with Weighted Least Squares](https://mlsanity.com/paper/2511.08852/) (2025)
- [Continent-wide Efficient and Fair Downlink Resource Allocation in LEO Satellite Constellations](https://mlsanity.com/paper/2211.06095/) (2022)
- [A Graph-Based Customizable Handover Framework for LEO Satellite Networks](https://mlsanity.com/paper/2211.07872/) (2022)

## 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.