車(chē)聯(lián)網(wǎng)廣播技術(shù)研究
發(fā)布時(shí)間：2019-06-19 12:06
【摘要】：近年來(lái),隨著經(jīng)濟(jì)、科技和汽車(chē)工業(yè)的迅猛發(fā)展,安全和效率已經(jīng)成為交通運(yùn)輸中迫切需要解決的兩個(gè)關(guān)鍵問(wèn)題。因此,智能交通系統(tǒng)應(yīng)運(yùn)而生。車(chē)聯(lián)網(wǎng)作為智能交通系統(tǒng)中的重要組成部分,對(duì)于提高智能交通系統(tǒng)的安全性和高效性具有十分重要的意義。車(chē)聯(lián)網(wǎng)具有節(jié)點(diǎn)高速移動(dòng)、網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)頻繁變化、連接可靠性差、傳輸時(shí)延大等特點(diǎn),這些特點(diǎn)使得廣播成為車(chē)聯(lián)網(wǎng)交通信息分發(fā)的最有效方式。然而道路交通中車(chē)輛速度、密度及網(wǎng)絡(luò)負(fù)載等網(wǎng)絡(luò)狀態(tài)的變化將會(huì)大大影響車(chē)聯(lián)網(wǎng)的廣播性能。但是,現(xiàn)有車(chē)聯(lián)網(wǎng)廣播技術(shù)的研究仍然無(wú)法根據(jù)這些網(wǎng)絡(luò)狀態(tài)的變化自適應(yīng)地調(diào)整媒體接入控制(Media Access Control, MAC)廣播機(jī)制,導(dǎo)致廣播風(fēng)暴頻繁發(fā)生、網(wǎng)絡(luò)資源分配不合理、無(wú)法滿(mǎn)足不同信息對(duì)服務(wù)質(zhì)量(Quality of Service,QoS)需求。為滿(mǎn)足車(chē)聯(lián)網(wǎng)廣播性能要求,提高廣播效率,本文在車(chē)輛與車(chē)輛(Vehicle-to-Vehicle,V2V)和車(chē)輛與路邊基礎(chǔ)設(shè)施(Vehicle-to-Infrastructure,V2I)兩個(gè)場(chǎng)景下基于不同網(wǎng)絡(luò)狀態(tài)重點(diǎn)研究了車(chē)聯(lián)網(wǎng)中的廣播技術(shù),尤其針對(duì)車(chē)聯(lián)網(wǎng)中的廣播性能從以下三方面進(jìn)行了重點(diǎn)分析。一、QoS和隊(duì)列管理是車(chē)聯(lián)網(wǎng)中廣播機(jī)制的關(guān)鍵問(wèn)題。缺乏MAC層中緩存隊(duì)列的建模及分析將無(wú)法準(zhǔn)確獲得廣播機(jī)制的重要QoS性能,進(jìn)而將無(wú)法全面了解此協(xié)議性能。本文在V2V場(chǎng)景下提出了一個(gè)二維馬爾科夫鏈模型用于分析不同網(wǎng)絡(luò)負(fù)載及規(guī)模下帶有有限緩存的車(chē)聯(lián)網(wǎng)系統(tǒng)中廣播機(jī)制的QoS性能。此外,使用一個(gè)簡(jiǎn)單的方法來(lái)求解二維馬爾科夫鏈模型的穩(wěn)態(tài)概率。通過(guò)分析表明,繁重的流量負(fù)載下,尤其當(dāng)網(wǎng)絡(luò)規(guī)模較大時(shí),MAC中二元指數(shù)回退和重傳的缺失導(dǎo)致了糟糕的QoS性能。通過(guò)合適的流量控制可以避免這樣的性能退化,為車(chē)聯(lián)網(wǎng)維持良好的QoS性能提供了流量控制指南。二、在Drive-Thru網(wǎng)絡(luò)中,根據(jù)距離路邊單元(Road-Side Unit, RSU)的遠(yuǎn)近不同,車(chē)輛節(jié)點(diǎn)會(huì)有不同的信噪比,進(jìn)而產(chǎn)生不同的傳輸速率。如果車(chē)輛節(jié)點(diǎn)無(wú)法根據(jù)傳輸速率的改變而自適應(yīng)地改變發(fā)包頻率,最小傳輸速率將會(huì)限制系統(tǒng)的性能,導(dǎo)致系統(tǒng)吞吐量低。對(duì)于此問(wèn)題,由于現(xiàn)在還沒(méi)有一個(gè)有效的MAC廣播機(jī)制解決此問(wèn)題,本文在V2I場(chǎng)景下提出了一個(gè)新的MAC機(jī)制用于稀疏高速公路環(huán)境中Drive-thru網(wǎng)絡(luò);并設(shè)計(jì)了一個(gè)馬爾科夫鏈模型用于分析車(chē)輛速度和密度對(duì)新廣播機(jī)制性能的影響。通過(guò)分析可以得出,當(dāng)車(chē)輛數(shù)較小時(shí),此MAC廣播機(jī)制能夠獲得較高的吞吐量并能緩和車(chē)輛移動(dòng)對(duì)于系統(tǒng)吞吐量的影響。大量的仿真實(shí)驗(yàn)驗(yàn)證了分析模型的精確性和MAC廣播機(jī)制的有效性。三、在實(shí)際的無(wú)線傳輸中,捕獲效應(yīng)是影響無(wú)線網(wǎng)絡(luò)性能的一個(gè)主要因素。缺乏捕獲效應(yīng)的建模及分析將無(wú)法獲得捕獲效應(yīng)對(duì)于無(wú)線網(wǎng)絡(luò)性能影響的評(píng)價(jià),尤其在考慮了車(chē)輛節(jié)點(diǎn)移動(dòng)的情況下。本文在V2I場(chǎng)景下提出了一個(gè)車(chē)輛網(wǎng)絡(luò)性能預(yù)測(cè)模型用于分析不同移動(dòng)速度及網(wǎng)絡(luò)規(guī)模下基于捕獲效應(yīng)的Drive-Thru網(wǎng)絡(luò)的性能。通過(guò)引進(jìn)一個(gè)車(chē)輛交通流模型,建模了道路上車(chē)輛的移動(dòng),描述了車(chē)輛速度、密度和到達(dá)率的關(guān)系,更準(zhǔn)確地模擬了實(shí)際道路上車(chē)輛的行駛情況。通過(guò)分析可以得出,使用此性能預(yù)測(cè)模型可以得到最優(yōu)競(jìng)爭(zhēng)窗口值。基于此最優(yōu)競(jìng)爭(zhēng)窗口值,車(chē)聯(lián)網(wǎng)可以達(dá)到最大的系統(tǒng)吞吐量而不過(guò)多浪費(fèi)競(jìng)爭(zhēng)時(shí)間。
[Abstract]:In recent years, with the rapid development of economy, science and technology and automobile industry, safety and efficiency have become two key problems in transportation. Therefore, the intelligent traffic system comes into being. As an important part of the intelligent traffic system, the vehicle networking is of great significance to improve the security and efficiency of the intelligent traffic system. The vehicle networking has the characteristics of high-speed movement of nodes, frequent changes of network topology, poor connection reliability and large transmission time delay. However, the change of vehicle speed, density and network load in road traffic will greatly affect the broadcasting performance of the vehicle. However, the research of the prior art of the prior art of the prior art is still unable to adjust the media access control (MAC) broadcast mechanism adaptively according to the change of the state of the network, resulting in the frequent occurrence of the broadcast storm and the unreasonable allocation of the network resources, The quality of service (QoS) requirements for different information cannot be met. In order to meet the requirements of the vehicle-to-network broadcasting performance and improve the broadcasting efficiency, the broadcasting technology in the vehicle-to-network is mainly studied in two scenarios of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2V) and vehicle-to-infrastructure (V2I). In particular, the broadcast performance in the vehicle networking is analyzed from the following three aspects. First, QoS and queue management are the key problems of the broadcasting mechanism in the vehicle networking. The lack of the modeling and analysis of the cache queue in the MAC layer will not be able to accurately obtain the important QoS performance of the broadcast mechanism, and the performance of this protocol will not be fully understood. In this paper, a two-dimensional Markov chain model is proposed to analyze the QoS performance of the broadcast mechanism in a vehicle-of-the-network system with limited cache under different network loads and scale. In addition, a simple method is used to solve the steady-state probability of the two-dimensional Markov chain model. The analysis shows that under the heavy traffic load, especially when the network size is large, the loss of the binary exponential back-off and retransmission in the MAC results in poor QoS performance. Such performance degradation can be avoided by suitable flow control, providing a flow control guide for maintaining good QoS performance for the vehicle networking. 2. In the Drive-Thru network, according to the distance from the road-side unit (RSU), the vehicle nodes will have different signal-to-noise ratios, thus generating different transmission rates. If the vehicle node is unable to adaptively change the contracted frequency according to the change of the transmission rate, the minimum transmission rate will limit the performance of the system, resulting in a low system throughput. In this paper, a new MAC mechanism is proposed in the V2I scenario for the drive-thru network in the sparse highway environment. And a Markov chain model is designed to analyze the effect of vehicle speed and density on the performance of the new broadcasting mechanism. By analysis, it can be concluded that, when the number of vehicles is small, this MAC broadcast mechanism can achieve higher throughput and mitigate the impact of vehicle movement on system throughput. A large number of simulation experiments verify the accuracy of the analytical model and the effectiveness of the MAC broadcast mechanism. In the actual wireless transmission, the capture effect is one of the main factors that affect the performance of the wireless network. The lack of modeling and analysis of the capture effect will not be able to obtain an evaluation of the impact of the capture effect on the performance of the wireless network, especially in the case of the movement of the vehicle node. In this paper, a vehicle network performance prediction model is presented to analyze the performance of the Drive-Thru network based on the capture effect at different speed and network scale. Through the introduction of a vehicle traffic flow model, the movement of the vehicle on the road is modeled, the relationship between the vehicle speed, the density and the arrival rate is described, and the running condition of the vehicle on the actual road is more accurately simulated. Through the analysis it can be concluded that the optimal competition window value can be obtained by using the performance prediction model. Based on this optimal competition window value, the vehicle networking can achieve the maximum system throughput without wasting the competition time.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：U495
本文編號(hào)：2502333
[Abstract]:In recent years, with the rapid development of economy, science and technology and automobile industry, safety and efficiency have become two key problems in transportation. Therefore, the intelligent traffic system comes into being. As an important part of the intelligent traffic system, the vehicle networking is of great significance to improve the security and efficiency of the intelligent traffic system. The vehicle networking has the characteristics of high-speed movement of nodes, frequent changes of network topology, poor connection reliability and large transmission time delay. However, the change of vehicle speed, density and network load in road traffic will greatly affect the broadcasting performance of the vehicle. However, the research of the prior art of the prior art of the prior art is still unable to adjust the media access control (MAC) broadcast mechanism adaptively according to the change of the state of the network, resulting in the frequent occurrence of the broadcast storm and the unreasonable allocation of the network resources, The quality of service (QoS) requirements for different information cannot be met. In order to meet the requirements of the vehicle-to-network broadcasting performance and improve the broadcasting efficiency, the broadcasting technology in the vehicle-to-network is mainly studied in two scenarios of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2V) and vehicle-to-infrastructure (V2I). In particular, the broadcast performance in the vehicle networking is analyzed from the following three aspects. First, QoS and queue management are the key problems of the broadcasting mechanism in the vehicle networking. The lack of the modeling and analysis of the cache queue in the MAC layer will not be able to accurately obtain the important QoS performance of the broadcast mechanism, and the performance of this protocol will not be fully understood. In this paper, a two-dimensional Markov chain model is proposed to analyze the QoS performance of the broadcast mechanism in a vehicle-of-the-network system with limited cache under different network loads and scale. In addition, a simple method is used to solve the steady-state probability of the two-dimensional Markov chain model. The analysis shows that under the heavy traffic load, especially when the network size is large, the loss of the binary exponential back-off and retransmission in the MAC results in poor QoS performance. Such performance degradation can be avoided by suitable flow control, providing a flow control guide for maintaining good QoS performance for the vehicle networking. 2. In the Drive-Thru network, according to the distance from the road-side unit (RSU), the vehicle nodes will have different signal-to-noise ratios, thus generating different transmission rates. If the vehicle node is unable to adaptively change the contracted frequency according to the change of the transmission rate, the minimum transmission rate will limit the performance of the system, resulting in a low system throughput. In this paper, a new MAC mechanism is proposed in the V2I scenario for the drive-thru network in the sparse highway environment. And a Markov chain model is designed to analyze the effect of vehicle speed and density on the performance of the new broadcasting mechanism. By analysis, it can be concluded that, when the number of vehicles is small, this MAC broadcast mechanism can achieve higher throughput and mitigate the impact of vehicle movement on system throughput. A large number of simulation experiments verify the accuracy of the analytical model and the effectiveness of the MAC broadcast mechanism. In the actual wireless transmission, the capture effect is one of the main factors that affect the performance of the wireless network. The lack of modeling and analysis of the capture effect will not be able to obtain an evaluation of the impact of the capture effect on the performance of the wireless network, especially in the case of the movement of the vehicle node. In this paper, a vehicle network performance prediction model is presented to analyze the performance of the Drive-Thru network based on the capture effect at different speed and network scale. Through the introduction of a vehicle traffic flow model, the movement of the vehicle on the road is modeled, the relationship between the vehicle speed, the density and the arrival rate is described, and the running condition of the vehicle on the actual road is more accurately simulated. Through the analysis it can be concluded that the optimal competition window value can be obtained by using the performance prediction model. Based on this optimal competition window value, the vehicle networking can achieve the maximum system throughput without wasting the competition time.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：U495
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