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A 5G mmWave-based task offloading approach for vehicular edge computing

Muhammet Tay Arafat Şentürk
Jun 2026 · Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi · 0 citations · 9 references

TL;DR

A 5G-based task offloading scheme is proposed, aiming to manage computational resources in a mobility-aware manner by considering vehicle mobility, and adapts task export decisions to changing network conditions, thereby improving system robustness and efficiency.

Abstract

Vehicle Edge Computing (VEC) is a computing paradigm specifically designed to support the execution of computationally intensive vehicle applications while ensuring low latency, efficient bandwidth utilization, and reduced energy consumption. In modern vehicular environments, vehicles are required to process large amounts of data generated by diverse applications, and local onboard resources are often insufficient to meet these demands. Therefore, vehicles must efficiently offload computational tasks to external computing systems that offer higher processing capabilities in order to maintain optimal computational performance, especially under dynamic and changing network conditions.This paper investigates a VEC scenario in which vehicles outsource their computational tasks with the objective of optimizing overall computation time and increasing the successful task transfer rate. In such scenarios, several factors affect computational efficiency, including heterogeneous task requirements and the inherent mobility of vehicles. Variations in task size, delay sensitivity, and processing demands, combined with frequent changes in vehicle location, can significantly affect the performance of task offloading mechanisms and resource utilization.To address these challenges, the study incorporates and utilizes a fifth-generation (5G) radio network to effectively manage task offloading decisions and computational resources. A 5G-based task offloading scheme is proposed, aiming to manage computational resources in a mobility-aware manner. By considering vehicle mobility, the proposed scheme adapts task export decisions to changing network conditions, thereby improving system robustness and efficiency. The scheme is further supported by a distributed communication model, which enables near-optimal solutions by coordinating task offloading and resource allocation across the network.Furthermore, the proposed approach integrates a fifth-generation new radio (NR) communication model to enhance system performance. This model includes both cellular connectivity and millimeter wave (mmWave) communication, allowing the system to benefit from the strengths of each communication mode. Simulation results demonstrate that the proposed model significantly improves computational efficiency, particularly in terms of successful task handover and Quality of Experience (QoE). QoE represents the overall service performance as perceived by users and is influenced by key factors such as latency, throughput, and error rates. Overall, the proposed approach achieves improved task offloading efficiency, reduces task failure rates, and enhances QoE through the effective integration of mmWave and 5G NR communication models.

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