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Handover Analysis for Vehicular Communication with Explainability on the Fly — arXiv:2608.14820

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.14820
License (as archived): CC0-1.0
Archived: 2026-10-18T17:55:00.783Z
SHA-256 of the archived text: af6051eb249348a9db7d9303036e7a606b36cd9685b1638bf6993eefa79c50b4
Stable URL of this page: https://metraipolis.com/documento/dc_74fe320e438c

How to cite

Handover Analysis for Vehicular Communication with Explainability on the Fly — arXiv:2608.14820 — arXiv (en), https://arxiv.org/abs/2608.14820 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_74fe320e438c (Metraipolis archive dc_74fe320e438c, sha256 af6051eb249348a9db7d9303036e7a606b36cd9685b1638bf6993eefa79c50b4)

BibTeX

@misc{dc74fe320e438c,
  title = {Handover Analysis for Vehicular Communication with Explainability on the Fly — arXiv:2608.14820},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.14820},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_74fe320e438c; sha256 af6051eb249348a9db7d9303036e7a606b36cd9685b1638bf6993eefa79c50b4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.14820. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-18 in cs.AI).
Categorie arXiv: Signal Processing (eess.SP) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.14820 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T16:29:13.960Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).