Deep Learning-aided Application Scheduler for Vehicular Safety Communication

TL;DR

This paper introduces a deep learning-based scheduler for vehicular safety communication that predicts channel activity to reduce packet collisions and enhance communication reliability in V2X networks.

Contribution

It presents a novel deep neural network approach to predict channel activity, improving collision avoidance in V2X communication systems.

Findings

Prediction of channel activity reduces packet collisions.

Improved communication performance in safety-related V2X services.

Effective handling of heterogeneous transmit patterns.

Abstract

802.11p based V2X communication uses stochastic medium access control, which cannot prevent broadcast packet collision, in particular during high channel load. Wireless congestion control has been designed to keep the channel load at an optimal point. However, vehicles' lack of precise and granular knowledge about true channel activity, in time and space, makes it impossible to fully avoid packet collisions. In this paper, we propose a machine learning approach using deep neural network for learning the vehicles' transmit patterns, and as such predicting future channel activity in space and time. We evaluate the performance of our proposal via simulation considering multiple safety-related V2X services involving heterogeneous transmit patterns. Our results show that predicting channel activity, and transmitting accordingly, reduces collisions and significantly improves communication performance.