Using Neural Network to Model and Estimate PER in Vehicular Ad Hoc Network

Abstract

Embedded with many advantages, the Wireless Access in Vehicular Environment (WAVE) was created to facilitate communication between vehicles and infrastructure (V2I) and among vehicles (V2V). Its ability to preserve equipment and lives was one of its most important and anticipated benefits. However, the complete integration of this life-saving technology into contemporary automobiles still faces technological obstacles after more than 10 years of intensive work by scientists and academia. The success of the WAVE completely relies on the regular exchange of Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs). Unfortunately, since CAMs and DENMs are broadcast by each mobile device in the network frequently, with the limited channel band of 10 MHz, communication resources quickly get exhausted due to broadcast storm issues, rendering further communication impossible. In any given scenario, if every mobile device involved could predict the Packet Error Rate (PER) based on factors such as mobility, Signal-to-Noise Ratio (SNR), and available bandwidth, then controlling the rate at which messages are broadcast might be possible, thus preventing or reducing broadcast storms. Consequently, the current work focuses on designing, developing, and modeling the PER in the VANET environment using Neural Network (NN). Based on the type and complexity of the challenge, the Levenberg-Marquardt Algorithm (LMA) was selected and used to develop the proposed system model. The response of the created and verified model demonstrated some efficacy and efficiency in predicting the PER based on the SNR and available bandwidth with up to 97% confidence.

Keywords

• Machine learning
• neural networks
• PER
• VANET
• V2I
• V2V