Research on Model and Simulation of Vehicle Network Based on LTE Technology

Xinyu Gai, Zhicheng Li, Yaqin Zhang, Shiqi Li, Guimin Meng, Lele Lv
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Abstract

Vehicle Ad Hoc Network (VANET) is one of the important means to realize intelligent transportation system, which can improve road safety and traffic efficiency. At present, two key technologies of vehicle communication include DSRC (Dedicated Short-Range Communication) and LTE (Long Term Evolution). After 20 years of in-depth research, DSRC has reached sufficient technical maturity, but its performance improvement space is relatively limited. LTE is a relatively new 3GPP-based standard and is considered the strongest competitor to IEEE 802.11p.There are many mature analysis models in DSRC reliability analysis, however as LTE is a newly emerging vehicle networking technology, the existing analysis models have some problems that do not conform to actual traffic scenarios and incomplete analysis of beacon transmission errors. Although technological innovations allow LTE to have many features over DSRC, studies have found that DSRC outperforms in some respects.Given the existing shortcomings of analysis models, this paper proposes an LTE broadcast performance analysis model based on the Poisson distribution traffic model. This model uses the Markov State Transition Process to evaluate the probability of packet reception for LTE combined with four transmission errors. The numerical results of the analysis model is cross-validated through the network simulation results obtained by the MATLAB simulator, which proves the correct-ness of the model.
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基于LTE技术的车辆网络模型与仿真研究
车辆自组织网络(VANET)是实现智能交通系统的重要手段之一,可以提高道路安全和交通效率。目前,车载通信的两大关键技术是DSRC(专用短程通信)和LTE(长期演进)。经过20年的深入研究,DSRC技术已经达到了足够的成熟度,但其性能提升空间相对有限。LTE是一种相对较新的基于3gpp的标准,被认为是IEEE 802.11p最有力的竞争对手。DSRC可靠性分析中有许多成熟的分析模型,但由于LTE是新兴的车联网技术,现有的分析模型存在不符合实际交通场景、对信标传输误差分析不全面等问题。尽管技术创新使LTE比DSRC拥有许多功能,但研究发现,DSRC在某些方面优于LTE。针对分析模型存在的不足,本文提出了一种基于泊松分布流量模型的LTE广播性能分析模型。该模型使用马尔可夫状态转换过程来评估LTE的分组接收概率,并结合四种传输错误。通过MATLAB仿真器获得的网络仿真结果,对分析模型的数值结果进行了交叉验证,验证了模型的正确性。
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