Affiliation:
1. School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China
2. Shenzhen Key Laboratory of Navigation and Communication Integration, Shenzhen 518107, China
Abstract
Vehicle-to-vehicle (V2V) communication has gained significant attention in the field of intelligent transportation systems. In this paper, we focus on communication scenarios involving vehicles moving in the same and opposite directions. Specifically, we model a V2V network as a dynamic multi-source single-sink network with two-way lanes. To address rapid changes in network topology, we employ random linear network coding (RLNC), which eliminates the need for knowledge of the network topology. We begin by deriving the lower bound for the generation probability. Through simulations, we analyzed the probability distribution and cumulative probability distribution of latency under varying packet loss rates and batch sizes. Our results demonstrated that our RLNC scheme significantly reduced the communication latency, even under challenging channel conditions, when compared to the non-coding case.
Funder
National Science Foundation of China
Science, Technology and Innovation Commission of Shenzhen Municipality
Subject
General Physics and Astronomy