Handover Analysis for Vehicular Communication with Explainability on the Fly

[2608.14820] Handover Analysis for Vehicular Communication with Explainability on the Fly Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2608.14820 (eess) [Submitted on 14 Aug 2026] Title: Handover Analysis for Vehicular Communication with Explainability on the Fly Authors: Ali Fuat Sahin , Semiha Tedik Başaran , Tufan Kumbasar View a PDF of the paper titled Handover Analysis for Vehicular Communication with Explainability on the Fly, by Ali Fuat Sahin and 2 other authors View PDF HTML (experimental) Abstract: 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. Comments: Accepted in NextGCom 2026, Copyright IEEE Subjects: Signal Processing (eess.SP) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14820 [eess.SP] (or arXiv:2608.14820v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2608.14820 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ali Fuat Sahin [ view email ] [v1] Fri, 14 Aug 2026 18:46:41 UTC (460 KB) Full-text links: Access Paper: View a PDF of the paper titled Handover Analysis for Vehicular Communication with Explainability on the Fly, by Ali Fuat Sahin and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• IS-IS Prefix Attributes for Extended IPv4 and IPv6 Reachability#502051
• Active Inference for Slow and Fast Interaction Loops in Holographic-Type Communication#1155445
• Making Time-Sensitive Networking Deployable: A Comprehensive Lifecycle Architecture#659067
• Making Time-Sensitive Networking Deployable: A Comprehensive Lifecycle Architecture#656931
• Making Time-Sensitive Networking Deployable: A Comprehensive Lifecycle Architecture#480021