Handover for V2V communication in 5G using convolutional neural networks

Abstract

Vehicle communication is one of the most vital aspects of modern transportation systems because it enables real-time data transmission between vehicles and infrastructure to improve traffic flow and road safety. The next generation of mobile technology, 5G, was created to address earlier generations' growing need for high data rates and quality of service issues. 5G cellular technology aims to eliminate penetration loss by segregating outside and inside settings and allowing extremely high transmission speeds, achieved by installing hundreds of dispersed antenna arrays using a distributed antenna system (DAS). Huge multiple-input multiple-output (MIMO) systems are accomplished via DASs and huge MIMO systems, where hundreds of dispersed antenna arrays are built. Because deep learning (DL) techniques employ artificial neural networks with at least one hidden layer, they are used in this study for vehicle recognition. They can swiftly process vast quantities of labeled training data to identify features. Therefore, this paper employed the VGG19 DL model through transfer learning to address the task of vehicle detection and obstacle identification. It also proposes a novel horizontal handover prediction method based on channel characteristics. The suggested techniques are designed for heterogeneous networks or horizontal handovers using DL. In the designated surrounding regions of 5G environments, the suggested detection and handover algorithms identified vehicles with a success rate of 97 % and predicted the next station for handover.

Keywords

• Connected autonomous vehicles (CAVs)
• Convolutional neural networks (CNN)
• Deep learning (DL)
• Received signal strength indicator (RSSI)