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

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.

A Comparative Study of IEEE 802.11bd and IEEE 802.11p on the Data Dissemination Properties in Dynamic Traffic Scenarios

“…The high-speed movement of vehicles introduces challenges such as high dynamics and spatiotemporal complexity, resulting in a significant Doppler shift and a rapidly varying wireless propagation environment [29,30]. Other factors, such as high density, resource collisions, proximity effects, and intricate interference conditions, further impact the reliability of data transmission.…”

Analysis of Unmanned Aerial Vehicle-Assisted Cellular Vehicle-to-Everything Communication Using Markovian Game in a Federated Learning Environment

“…The reliability-latency model specifies that when the Nash flow is positive for any packet or all packets that have positive flow at Nash equilibrium, they will have specified reliability and latency [42]. Flows at Nash equilibrium have a property that all packets within one window experience the same delay for a reliability-latency function [43]. The authors in [13] have demonstrated that the reliability-latency model of sojourn time to reach traffic equilibrium can be calculated using Nash equilibrium.…”