# Explainable AI for UAV Mobility Management: A Deep Q-Network Approach for Handover Minimization

arXiv: https://arxiv.org/abs/2504.18371 · HTML: https://mlsanity.com/paper/2504.18371/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 25 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Explanation-Guided Deep Reinforcement Learning for Trustworthy 6G RAN Slicing](https://mlsanity.com/paper/2303.15000/) (2023)
- [Efficient Drone Mobility Support Using Reinforcement Learning](https://mlsanity.com/paper/1911.09715/) (2019)
- [Machine Learning assisted Handover and Resource Management for Cellular Connected Drones](https://mlsanity.com/paper/2001.07937/) (2020)

## Abstract (arXiv)

The integration of unmanned aerial vehicles (UAVs) into cellular networks presents significant mobility management challenges, primarily due to frequent handovers caused by probabilistic line-of-sight conditions with multiple ground base stations (BSs). To tackle these challenges, reinforcement learning (RL)-based methods, particularly deep Q-networks (DQN), have been employed to optimize handover decisions dynamically. However, a major drawback of these learning-based approaches is their black-box nature, which limits interpretability in the decision-making process. This paper introduces an explainable AI (XAI) framework that incorporates Shapley Additive Explanations (SHAP) to provide deeper insights into how various state parameters influence handover decisions in a DQN-based mobility management system. By quantifying the impact of key features such as reference signal received power (RSRP), reference signal received quality (RSRQ), buffer status, and UAV position, our approach enhances the interpretability and reliability of RL-based handover solutions. To validate and compare our framework, we utilize real-world network performance data collected from UAV flight trials. Simulation results show that our method provides intuitive explanations for policy decisions, effectively bridging the gap between AI-driven models and human decision-makers.