Zhang Yiqian, Zhu Tiantian, Li Congduan
School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China.
Shenzhen Key Laboratory of Navigation and Communication Integration, Shenzhen 518107, China.
Entropy (Basel). 2023 Oct 17;25(10):1454. doi: 10.3390/e25101454.
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.
车对车(V2V)通信在智能交通系统领域受到了广泛关注。在本文中,我们专注于涉及车辆同向和反向行驶的通信场景。具体而言,我们将V2V网络建模为具有双向车道的动态多源单汇网络。为应对网络拓扑的快速变化,我们采用随机线性网络编码(RLNC),它无需了解网络拓扑。我们首先推导生成概率的下限。通过仿真,我们分析了不同丢包率和批量大小下延迟的概率分布和累积概率分布。我们的结果表明,与非编码情况相比,即使在具有挑战性的信道条件下,我们的RLNC方案也显著降低了通信延迟。