Open Access

ARTICLE

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

by Huanhuan Li^1,2,*, Hongchang Wei², Zheliang Chen², Yue Xu³

1 School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China
2 Department of Electrical Engineering, Jiangxi Vocational College of Mechanical & Electrical Technology, Nanchang, 330013, China
3 Kehua Data Co., Ltd., Xiamen, 361006, China

* Corresponding Author: Huanhuan Li. Email:

(This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)

Computers, Materials & Continua 2024, 80(2), 2199-2219. https://doi.org/10.32604/cmc.2024.052155

Received 25 March 2024; Accepted 24 June 2024; Issue published 15 August 2024

Abstract

The current resource allocation in 5G vehicular networks for mobile cloud communication faces several challenges, such as low user utilization, unbalanced resource allocation, and extended adaptive allocation time. We propose an adaptive allocation algorithm for mobile cloud communication resources in 5G vehicular networks to address these issues. This study analyzes the components of the 5G vehicular network architecture to determine the performance of different components. It is ascertained that the communication modes in 5G vehicular networks for mobile cloud communication include in-band and out-of-band modes. Furthermore, this study analyzes the single-hop and multi-hop modes in mobile cloud communication and calculates the resource transmission rate and bandwidth in different communication modes. The study also determines the scenario of one-way and two-way vehicle lane cloud communication network connectivity, calculates the probability of vehicle network connectivity under different mobile cloud communication radii, and determines the amount of cloud communication resources required by vehicles in different lane scenarios. Based on the communication status of users in 5G vehicular networks, this study calculates the bandwidth and transmission rate of the allocated channels using Shannon’s formula. It determines the adaptive allocation of cloud communication resources, introduces an objective function to obtain the optimal solution after allocation, and completes the adaptive allocation process. The experimental results demonstrate that, with the application of the proposed method, the maximum utilization of user communication resources reaches approximately 99%. The balance coefficient curve approaches 1, and the allocation time remains under 2 s. This indicates that the proposed method has higher adaptive allocation efficiency.

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Keywords

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