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

• URL: http://arxiv.org/abs/2504.18371v1
• Date: Fri, 25 Apr 2025 14:11:51 GMT
• Title: Explainable AI for UAV Mobility Management: A Deep Q-Network Approach for Handover Minimization
• Authors: Irshad A. Meer, Bruno Hörmann, Mustafa Ozger, Fabien Geyer, Alberto Viseras, Dominic Schupke, Cicek Cavdar,
• Abstract summary: This paper introduces an explainable AI (XAI) framework to provide deeper insights into how various state parameters influence handover decisions.<n>By quantifying the impact of key features such as reference signal received power (RSRP), our approach enhances the interpretability and reliability of RL-based handover solutions.
• Score: 4.7430397428031785
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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