Enhancing the Reliability of Communication between Vehicle and Everything (V2X) Based on Deep Learning for Providing Efficient Road Traffic Information

Radwa Ahmed Osman,Mazen Nabil Elagamy,Yasmine N M Saleh,Sherine Nagy Saleh

doi:10.3390/app112311382

Radwa Ahmed Osman, Mazen Nabil Elagamy + Show 2 more

Open Access

https://doi.org/10.3390/app112311382

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Abstract

Developing efficient communication between vehicles and everything (V2X) is a challenging task, mainly due to the characteristics of vehicular networks, which include rapid topology changes, large-scale sizes, and frequent link disconnections. This article proposes a deep learning model to enhance V2X communication. Various channel conditions such as interference, channel noise, and path loss affect the communication between a vehicle (V) and everything (X). Thus, the proposed model aims to determine the required optimum interference power to enhance connectivity, comply with the quality of service (QoS) constraints, and improve the communication link reliability. The proposed model fulfills the best QoS in terms of four metrics, namely, achievable data rate (Rb), packet delivery ratio (PDR), packet loss rate (PLR), and average end-to-end delay (E2E). The factors to be considered are the distribution and density of vehicles, average length, and minimum safety distance between vehicles. A mathematical formulation of the optimum required interference power is presented to achieve the given objectives as a constrained optimization problem, and accordingly, the proposed deep learning model is trained. The obtained results show the ability of the proposed model to enhance the connectivity between V2X for improving road traffic information efficiency and increasing road traffic safety.

Highlights

The development of intelligent transport systems (ITS) is expected to project benefits such as traffic safety, lower congestion, various services offered to users while overcoming the disorganization and inefficiency of traditional systems [1]
An interference power control model for V2X based on a deep learning algorithm was proposed to enhance the connectivity between V and X, providing efficient road traffic communications
The proposed model is expressed using the Lagrange optimization technique to determine the optimum required interference power, satisfying the system requirements. Based on this optimization model, the system quality of service (QoS) was evaluated in terms of reliability and effectiveness for V2X communications

Summary

Introduction

The development of intelligent transport systems (ITS) is expected to project benefits such as traffic safety, lower congestion, various services offered to users while overcoming the disorganization and inefficiency of traditional systems [1]. An interference power control is developed through the proposed approach to enhance system performance in terms of achievable data rate, packet delivery ratio, packet loss rate, and average end-to-end delay for the V2X communication. The proposed approach is evaluated in terms of four metrics, which are achievable data rate, packet delivery ratio, packet loss rate, and average end-to-end delay under different channel conditions, such as vehicle transmission power (PV), required QoS, different SINRth values, path loss, and different transmission distances. Through these findings, the entire network can be optimized under different environmental conditions.

Material and Methods

Proposed Analytical Model

Simulation of Analytical Model

Conclusions