Handover Analysis for Vehicular Communication with Explainability on the Fly

Ali Fuat Sahin; Semiha Tedik Başaran; Tufan Kumbasar

eess.SPAug 14, 2026

Handover Analysis for Vehicular Communication with Explainability on the Fly

Authors: Ali Fuat Sahin, Semiha Tedik Başaran, Tufan Kumbasar

Organizations: Faculty of Electrical and Electronics Engineering, Istanbul Technical University, Istanbul, Turkiye · AI and Intelligent Systems Laboratory, Istanbul Technical University, Istanbul, Turkiye

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.

Figures & tables

Features	Dataset Name
	Beyond Throughput	DoNext
Number of Samples	2508	152,775
Number of Features	25	28
Number of Handovers (%)	873 (34.81%)	59,472 (38.93%)
Cellular Technology	5G NR $\&$ LTE	5G NR
SC/NC Metrics	Yes	No (Only SC)

TABLE I : Dataset Properties

KPI	Description
Common Features
Cell Index	Serving cell identifier.
Location (3 Features)	Longitude, latitude, and altitude.
Velocity $\&$ $\Delta$ Velocity	Current velocity of the UE (km/h).
RSRP $\&$ $\Delta$ RSRP	Reference Signal Received Power (dBm).
RSRQ $\&$ $\Delta$ RSRQ	Reference Signal Received Quality (dB).

TABLE II : The KPIs for the Handover Analysis

Method	Beyond Throughput Dataset							DoNext Dataset
Method	Acc. (Train/Test)		AUC	Prec.	Rec.	F1-Score	Runtime [s]	Acc. (Train/Test)		AUC	Prec.	Rec.	F1-Score	Runtime [s]
GAM	0.894	0.837	0.920	0.796	0.742	0.767	110.9	0.813	0.779	0.867	0.733	0.679	0.705	222.5
EBM	0.946	0.835	0.913	0.776	0.738	0.755	5.9	0.866	0.781	0.861	0.757	0.673	0.712	6.3
GAMI-Net	0.846	0.826	0.908	0.785	0.715	0.747	327.6	0.788	0.779	0.865	0.724	0.698	0.728	1660.2
LSTM+SHAP	0.944	0.914	0.938	0.879	0.862	0.870	13.8/1915.3	0.842	0.804	0.865	0.792	0.674	0.727	58.2/1932.7

TABLE III : Performance Comparison of Models on Handover Datasets

Fig. 2 : Accuracy-latency chart.

	Beyond Throughput	DoNext
GAM	Cell Index	TrackingAreaCode
	State	SS-RSRP
	Longitude	Bearing
	RSRP	SS-RSRQ
	BitrateUL	RSRQ
EBM	$\Delta$ NRxRSRQ	$\Delta$ RSRP

TABLE IV : Influential KPIs

Panel of Figure 7 — Fig. 3 : GAMI-Net visualizations for Distance and RSRQ -related KPIs for the BT dataset.

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Intelligent Transportation Systems Explainable Image Classification Fine-Grained Visual Perception Image Classification

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In the area of Intelligent Transportation Systems (ITS), fine-grained vehicle classification systems play an essential role. Recently, the authors have presented a novel vision-based classification approach in which standard end-to-end Convolutional Neural Networks (CNNs) have been decomposed into 1) a CNN-based detector for semantically strong vehicle parts, followed by 2) feature construction and 3) final classification by a decision tree. In contrast to conventional CNNs, this allows both easy extensibility to new vehicle categories - without the need to fully retrain the part detector - and an important step towards the interpretability of the model, removing partially the black-box nature inherent to CNNs. Here we present an important extension of this approach that now incorporates spatial awareness of the vehicle parts: while the feature construction 2) of the previous approach used a binary decision for each feature (present vs. absent), now a full spatial probability map is constructed to condition the presence of each individual part with respect to a given vehicle category. The classification is performed using a softmax regression approach for the overall vehicle probabilities. This method shows a considerably improved robustness against false (part-)detections, a point that is crucial for practical application. Comparative analyses with a state-of-the-art end-to-end CNN indicate that our part-based methods achieve comparable accuracy, effectively challenging the presumed trade-off between accuracy and explainability. This research represents a significant advance in vehicle classification for ITS and forms the basis for systems that combine high accuracy with intuitive interpretability.

Abstract

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