# Handover Analysis for Vehicular Communication with Explainability on the Fly

arXiv: https://arxiv.org/abs/2608.14820 · HTML: https://mlsanity.com/paper/2608.14820/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions](https://mlsanity.com/paper/2003.07132/) (2020)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models](https://mlsanity.com/paper/2409.14429/) (2024)
- [Handover Configurations in Operational 5G Networks: Diversity, Evolution, and Impact on Performance](https://mlsanity.com/paper/2511.03116/) (2025)
- [RIS Meets O-RAN: A Practical Demonstration of Multi-user RIS Optimization through RIC](https://mlsanity.com/paper/2501.18917/) (2025)
- [An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing](https://mlsanity.com/paper/2506.11882/) (2025)

## Abstract (arXiv)

Handover (HO) management in vehicular networks requires fast and reliable decision-making under highly dynamic conditions. While machine learning (ML) approaches can improve HO detection by capturing complex relationships among various key performance indicators (KPIs), their black-box nature limits interpretability and operator trust. To address this, this paper investigates HO detection from an explainability-on-the-fly perspective using inherently interpretable models based on the functional analysis of variance (fANOVA) framework. The proposed models are evaluated using two real-world operator datasets and compared against a Long Short-Term Memory baseline augmented with post-hoc SHAP explanations. Unlike post-hoc approaches, the proposed framework enables immediate interpretation of model decisions without incurring additional computational overhead. This capability is particularly critical for latency-sensitive vehicular networks. The results show that fANOVA-based models achieve competitive detection performance while providing significantly reduced explanation latency compared to conventional post-hoc methods. Furthermore, feature ranking and visualization analyses reveal physically meaningful relationships between KPIs and HO occurrences that align with standardized HO mechanisms. These results demonstrate that inherently interpretable models provide an efficient and transparent solution for HO detection in next-generation vehicular networks.