Open Access
ARTICLE
Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication
Huanhuan Li1,2,*, 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: huanz1224@163.com
(This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.052155
Received 25 March 2024; Accepted 24 June 2024; Published online 17 July 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.
Keywords
5G vehicular networks; mobile cloud communication; resource allocation; channel capacity; network connectivity; communication radius; objective function
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