Packet Error Rate Prediction in VANETs using Bandwidth and Signal-to-Noise Ratio
Keywords:
Machine Learning, Neural Networks, PER, VANET, WAVE, V2V, V2IAbstract
The Wireless Access in Vehicular Environment (WAVE) aims to facilitate communication between vehicles and infrastructure, primarily for safety improvements. Despite more than a decade of research, significant technological hurdles still remain before this potentially life-saving technology can be seamlessly integrated into modern automobiles. However, the frequent transmission of Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs) from each mobile device is the leading cause of broadcast storms, especially considering the limited 10 MHz channel bandwidth. Therefore, to mitigate some of these challenges, making use of the Feed Forward Neural Network (FFNN) and the Levenberg-Marquardt Algorithm (LMA), the current work focuses on designing, developing, and modeling Packet Error Rate (PER) in the Vehicular Ad hoc Network (VANET) environment to enable mobile devices to predict the PER based on such factors as the available Bandwidth (BW) and Signal-to-Noise Ratio (SNR). Following model development and training, rigorous testing and verification are conducted to demonstrate the system's effectiveness and efficiency in predicting PER based on SNR and BW parameters. The resulting evaluation proved that the developed model has the ability to accurately predict PER with an accuracy of 85%.
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