# Handover Protocol Learning for LEO Satellite Networks: Access Delay and Collision Minimization

arXiv: https://arxiv.org/abs/2310.20215 · HTML: https://mlsanity.com/paper/2310.20215/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach](https://mlsanity.com/paper/1810.11758/) (2018)
- [CubeSat Communications: Recent Advances and Future Challenges](https://mlsanity.com/paper/1908.09501/) (2019)
- [Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks](https://mlsanity.com/paper/2010.10138/) (2020)
- [Learning-based Handover in Mobile Millimeter-wave Networks](https://mlsanity.com/paper/2003.11009/) (2020)
- [Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks](https://mlsanity.com/paper/2005.12521/) (2020)
- [A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems](https://mlsanity.com/paper/1604.04248/) (2016)
- [Learning Emergent Random Access Protocol for LEO Satellite Networks](https://mlsanity.com/paper/2112.01765/) (2021)

## Built on by (most cited first, 4 of 4)

- [Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning](https://mlsanity.com/paper/2404.07450/) (2024)
- [On-Device LLM for Context-Aware Wi-Fi Roaming](https://mlsanity.com/paper/2505.04174/) (2025)
- [DRL-Based Beam Positioning for LEO Satellite Constellations with Weighted Least Squares](https://mlsanity.com/paper/2511.08852/) (2025)
- [ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems](https://mlsanity.com/paper/2608.08804/) (2026)

## Abstract (arXiv)

This study presents a novel deep reinforcement learning (DRL)-based handover (HO) protocol, called DHO, specifically designed to address the persistent challenge of long propagation delays in low-Earth orbit (LEO) satellite networks' HO procedures. DHO skips the Measurement Report (MR) in the HO procedure by leveraging its predictive capabilities after being trained with a pre-determined LEO satellite orbital pattern. This simplification eliminates the propagation delay incurred during the MR phase, while still providing effective HO decisions. The proposed DHO outperforms the legacy HO protocol across diverse network conditions in terms of access delay, collision rate, and handover success rate, demonstrating the practical applicability of DHO in real-world networks. Furthermore, the study examines the trade-off between access delay and collision rate and also evaluates the training performance and convergence of DHO using various DRL algorithms.