Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Department of Computer Technology, Technical College, Technical and Vocational Training Corporation, Riyadh 11472, Saudi Arabia.
Sensors (Basel). 2021 Sep 23;21(19):6361. doi: 10.3390/s21196361.
The ultra-dense network (UDN) is one of the key technologies in fifth generation (5G) networks. It is used to enhance the system capacity issue by deploying small cells at high density. In 5G UDNs, the cell selection process requires high computational complexity, so it is considered to be an open NP-hard problem. Internet of Vehicles (IoV) technology has become a new trend that aims to connect vehicles, people, infrastructure and networks to improve a transportation system. In this paper, we propose a machine-learning and IoV-based cell selection scheme called Artificial Neural Network Cell Selection (ANN-CS). It aims to select the small cell that has the longest dwell time. A feed-forward back-propagation ANN (FFBP-ANN) was trained to perform the selection task, based on moving vehicle information. Real datasets of vehicles and base stations (BSs), collected in Los Angeles, were used for training and evaluation purposes. Simulation results show that the trained ANN model has high accuracy, with a very low percentage of errors. In addition, the proposed ANN-CS decreases the handover rate by up to 33.33% and increases the dwell time by up to 15.47%, thereby minimizing the number of unsuccessful and unnecessary handovers (HOs). Furthermore, it led to an enhancement in terms of the downlink throughput achieved by vehicles.
超密集网络（UDN）是第五代（5G）网络的关键技术之一。它通过在高密度部署小小区来增强系统容量问题。在 5G UDN 中，小区选择过程需要高的计算复杂度，因此被认为是一个开放的 NP 难问题。车联网（IoV）技术已成为一种新趋势，旨在连接车辆、人员、基础设施和网络，以改善交通系统。在本文中，我们提出了一种基于机器学习和 IoV 的小区选择方案，称为人工神经网络小区选择（ANN-CS）。它旨在选择驻留时间最长的小小区。基于移动车辆信息，训练前馈反向传播神经网络（FFBP-ANN）来执行选择任务。使用在洛杉矶收集的车辆和基站（BS）的真实数据集进行训练和评估。仿真结果表明，训练好的 ANN 模型具有很高的准确性，错误率非常低。此外，所提出的 ANN-CS 可以将切换率降低多达 33.33%，并将驻留时间延长多达 15.47%，从而最小化了不成功和不必要的切换（HO）的数量。此外，它还提高了车辆的下行链路吞吐量。
