Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918492273377280 |
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| author | Chou, Po-Heng Wang, Chiapin Huang, Chung-Chi Chen, Kuan-Hao |
| author_facet | Chou, Po-Heng Wang, Chiapin Huang, Chung-Chi Chen, Kuan-Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02416 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks Chou, Po-Heng Wang, Chiapin Huang, Chung-Chi Chen, Kuan-Hao Information Theory Machine Learning 68M20, 68T05 C.2.1; C.2.3; I.2.6 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. |
| title | Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks |
| topic | Information Theory Machine Learning 68M20, 68T05 C.2.1; C.2.3; I.2.6 |
| url | https://arxiv.org/abs/2605.02416 |