With the development of automated driving vehicles, more and more vehicles will be fitted with more than one automotive radars, and the radar mutual interference will become very significant. Vehicle to everything (V2...With the development of automated driving vehicles, more and more vehicles will be fitted with more than one automotive radars, and the radar mutual interference will become very significant. Vehicle to everything (V2X) communication is a potential way for coordinating automotive radars and reduce the mutual interference. In this paper, we analyze the positional relation of the two radars that interfere with each other, and evaluate the mutual interference for different types of automotive radars based on Poisson point process (PPP). We also propose a centralized framework and the corresponding algorithm, which relies on V2X communication systems to allocate the spectrum resources for automotive radars to minimize the interference. The minimum spectrum resources required for zero-interference are analyzed for different cases. Simulation results validate the analysis and show that the proposed framework can achieve near-zero-interference with the minimum spectrum resources.展开更多
As the vehicles gain the extensive popularity and increasing demand, traffic accident is one of the most serious problems faced by modem transportation system. Hereinto, crashes between cars and pedestrians cause plen...As the vehicles gain the extensive popularity and increasing demand, traffic accident is one of the most serious problems faced by modem transportation system. Hereinto, crashes between cars and pedestrians cause plenty of injuries and even death. Diverting attention from walking to smartphones is one of the main reasons for pedestrians getting injured by vehicles. However, the traditional measures protecting pedestrians from the vehicles heavily rely on the sound warning method, which do not capable for pedestrians focusing on the smartphones. As the smartphones become ubiquitous and intelligent, they have the capacity to provide alert for the pedestrians with the help of vehicle-to-pedestrian （V2P） communication. In this paper, an efficient vehicle-to-X （V2X） communication system is designed for the vehicle and pedestrian communication to guarantee the safety of people. It achieves the IEEE 802.11p and the WiFi protocols meanwhile on the on-board unit （OBU） designed for vehicles. Extensive evaluation shows that the OBU can provide the reliable communication for vehicle-to-vehicle （V2V） and V2P in terms of packet delivery rate and average delay. Furthermore, two safety applications have been developed to protect the safety of vehicles and pedestrians based on the data transferred from the OBU. The first application is designed to show the driving information and provide the collision forewaming alert on the tablet within the vehicle. The second application is developed for the smartphone to provide forewarning alert information to the smartphone-distracted vulnerable pedestrians. Smartphone states are appreciable to provide the adaptive alert modes. Experimental results show that these applications are capable of alerting the intersection accidents, and the pedestrians can get the adaptive alerts according to smartphone usage contexts.展开更多
In Heterogeneous Vehicle-to-Everything Networks(HVNs),multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services.However,the increasing num...In Heterogeneous Vehicle-to-Everything Networks(HVNs),multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services.However,the increasing number of entities accessing HVNs presents a huge technical challenge to allocate the limited wireless resources.Traditional model-driven resource allocation approaches are no longer applicable because of rich data and the interference problem of multiple communication modes reusing resources in HVNs.In this paper,we investigate a wireless resource allocation scheme including power control and spectrum allocation based on the resource block reuse strategy.To meet the high capacity of cellular users and the high reliability of Vehicle-to-Vehicle(V2V)user pairs,we propose a data-driven Multi-Agent Deep Reinforcement Learning(MADRL)resource allocation scheme for the HVN.Simulation results demonstrate that compared to existing algorithms,the proposed MADRL-based scheme achieves a high sum capacity and probability of successful V2V transmission,while providing close-to-limit performance.展开更多
In recent years, research has been conducted on connected vehicles (CVs) that are equipped with communication devices and can be connected to networks. CVs share their own position information and surrounding informat...In recent years, research has been conducted on connected vehicles (CVs) that are equipped with communication devices and can be connected to networks. CVs share their own position information and surrounding information with other vehicles using Vehicle-to-Everything (V2X) communication. CVs can recognize obstacles on non-line-of-sight (NLoS), which cannot be recognized by autonomous vehicles, and reduce travel time to a destination by cooperative driving. Therefore, CVs are expected to provide safe and efficient transportation. On the other hand, problems of security of V2X communication by CVs have been discussed. Safe and efficient transportation by </span><span style="font-family:Verdana;">CVs is on the basis of the assumption that correct vehicle information is </span><span style="font-family:Verdana;">shared. If fake vehicle information is shared, it will affect the driving of CVs. In particular, vehicle position faking has been shown that it can induce traffic congestion and accidents, which is a serious problem. </span><span style="font-family:Verdana;">In this study, we define position faking by CV as misbehavior and propose a method to detect misbehavior on the basis of changes in vehicle position time series data composed of vehicle position information. We evaluated the proposed method using four different misbehavior models. F-measure of misbehavior models that CV sends random position information detected by the proposed method is higher than one by a related method. Therefore, the proposed method </span><span style="font-family:Verdana;">is suitable for detecting misbehavior in which the position information</span><span style="font-family:Verdana;"> changes over time.展开更多
In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)environ...In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)environments.In the proposed sum-of-sinusoids(SoS)channel model,the waves that emerge from the transmitter undergo line-of-sight(LoS)and non-line-of-sight(NLoS)propagation to the receiver,which makes the model suitable for describing numerous V2X wireless communication scenarios for sixth-generation(6G).We derive expressions for the real and imaginary parts of the complex channel impulse response(CIR),which characterize the physical propagation characteristics of V2X wireless channels.The statistical properties of the real and imaginary parts of the complex CIRs,i.e.,autocorrelation functions(ACFs),Doppler power spectral densities(PSDs),cross-correlation functions(CCFs),and variances of ACFs and CCFs,are derived and discussed.Simulation results are generated and match those predicted by the underlying theory,demonstrating the accuracy of our derivation and analysis.The proposed framework and underlying theory arise as an efficient tool to investigate the statistical properties of 6G MIMO V2X communication systems.展开更多
In this paper,we investigate the problem of fast spectrum sharing in vehicle-to-everything com-munication.In order to improve the spectrum effi-ciency of the whole system,the spectrum of vehicle-to-infrastructure link...In this paper,we investigate the problem of fast spectrum sharing in vehicle-to-everything com-munication.In order to improve the spectrum effi-ciency of the whole system,the spectrum of vehicle-to-infrastructure links is reused by vehicle-to-vehicle links.To this end,we model it as a problem of deep reinforcement learning and tackle it with prox-imal policy optimization.A considerable number of interactions are often required for training an agent with good performance,so simulation-based training is commonly used in communication networks.Nev-ertheless,severe performance degradation may occur when the agent is directly deployed in the real world,even though it can perform well on the simulator,due to the reality gap between the simulation and the real environments.To address this issue,we make prelim-inary efforts by proposing an algorithm based on meta reinforcement learning.This algorithm enables the agent to rapidly adapt to a new task with the knowl-edge extracted from similar tasks,leading to fewer in-teractions and less training time.Numerical results show that our method achieves near-optimal perfor-mance and exhibits rapid convergence.展开更多
基金support by China Information Communication Technologies Group Corporationsupported in part by Chinese Ministry of Education-China Mobile Communication Corporation Research Fund under Grant MCM20170101the European Union’s Horizon 2020 research and innovation programme under the Marie Skldowska-Curie Grant Agreement No.793345
文摘With the development of automated driving vehicles, more and more vehicles will be fitted with more than one automotive radars, and the radar mutual interference will become very significant. Vehicle to everything (V2X) communication is a potential way for coordinating automotive radars and reduce the mutual interference. In this paper, we analyze the positional relation of the two radars that interfere with each other, and evaluate the mutual interference for different types of automotive radars based on Poisson point process (PPP). We also propose a centralized framework and the corresponding algorithm, which relies on V2X communication systems to allocate the spectrum resources for automotive radars to minimize the interference. The minimum spectrum resources required for zero-interference are analyzed for different cases. Simulation results validate the analysis and show that the proposed framework can achieve near-zero-interference with the minimum spectrum resources.
基金supported by the National Natural Science Foundation of China (61502045, 61201149)the National Science and Technology Major Project (2013ZX03001026-002, 2015ZX03001030)+3 种基金the EU FP7IRSES Mobile Cloud Project (612212)the 111 Project (B08004)the Fundamental Research Funds for the Central Universitiesthe Beijing Higher Education Young Elite Teacher Project
文摘As the vehicles gain the extensive popularity and increasing demand, traffic accident is one of the most serious problems faced by modem transportation system. Hereinto, crashes between cars and pedestrians cause plenty of injuries and even death. Diverting attention from walking to smartphones is one of the main reasons for pedestrians getting injured by vehicles. However, the traditional measures protecting pedestrians from the vehicles heavily rely on the sound warning method, which do not capable for pedestrians focusing on the smartphones. As the smartphones become ubiquitous and intelligent, they have the capacity to provide alert for the pedestrians with the help of vehicle-to-pedestrian （V2P） communication. In this paper, an efficient vehicle-to-X （V2X） communication system is designed for the vehicle and pedestrian communication to guarantee the safety of people. It achieves the IEEE 802.11p and the WiFi protocols meanwhile on the on-board unit （OBU） designed for vehicles. Extensive evaluation shows that the OBU can provide the reliable communication for vehicle-to-vehicle （V2V） and V2P in terms of packet delivery rate and average delay. Furthermore, two safety applications have been developed to protect the safety of vehicles and pedestrians based on the data transferred from the OBU. The first application is designed to show the driving information and provide the collision forewaming alert on the tablet within the vehicle. The second application is developed for the smartphone to provide forewarning alert information to the smartphone-distracted vulnerable pedestrians. Smartphone states are appreciable to provide the adaptive alert modes. Experimental results show that these applications are capable of alerting the intersection accidents, and the pedestrians can get the adaptive alerts according to smartphone usage contexts.
基金funded in part by the National Key Research and Development of China Project (2020YFB1807204)in part by National Natural Science Foundation of China (U2001213 and 61971191)+1 种基金in part by the Beijing Natural Science Foundation under Grant L201011in part by the key project of Natural Science Foundation of Jiangxi Province (20202ACBL202006)。
文摘In Heterogeneous Vehicle-to-Everything Networks(HVNs),multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services.However,the increasing number of entities accessing HVNs presents a huge technical challenge to allocate the limited wireless resources.Traditional model-driven resource allocation approaches are no longer applicable because of rich data and the interference problem of multiple communication modes reusing resources in HVNs.In this paper,we investigate a wireless resource allocation scheme including power control and spectrum allocation based on the resource block reuse strategy.To meet the high capacity of cellular users and the high reliability of Vehicle-to-Vehicle(V2V)user pairs,we propose a data-driven Multi-Agent Deep Reinforcement Learning(MADRL)resource allocation scheme for the HVN.Simulation results demonstrate that compared to existing algorithms,the proposed MADRL-based scheme achieves a high sum capacity and probability of successful V2V transmission,while providing close-to-limit performance.
文摘In recent years, research has been conducted on connected vehicles (CVs) that are equipped with communication devices and can be connected to networks. CVs share their own position information and surrounding information with other vehicles using Vehicle-to-Everything (V2X) communication. CVs can recognize obstacles on non-line-of-sight (NLoS), which cannot be recognized by autonomous vehicles, and reduce travel time to a destination by cooperative driving. Therefore, CVs are expected to provide safe and efficient transportation. On the other hand, problems of security of V2X communication by CVs have been discussed. Safe and efficient transportation by </span><span style="font-family:Verdana;">CVs is on the basis of the assumption that correct vehicle information is </span><span style="font-family:Verdana;">shared. If fake vehicle information is shared, it will affect the driving of CVs. In particular, vehicle position faking has been shown that it can induce traffic congestion and accidents, which is a serious problem. </span><span style="font-family:Verdana;">In this study, we define position faking by CV as misbehavior and propose a method to detect misbehavior on the basis of changes in vehicle position time series data composed of vehicle position information. We evaluated the proposed method using four different misbehavior models. F-measure of misbehavior models that CV sends random position information detected by the proposed method is higher than one by a related method. Therefore, the proposed method </span><span style="font-family:Verdana;">is suitable for detecting misbehavior in which the position information</span><span style="font-family:Verdana;"> changes over time.
基金supported by National Natural Science Foundation of China(NSFC)(No.62101274 and 62101275)Natural Science Foundation of Jiangsu Province(BK20210640)Open Research Fund of National Mobile Communications Research Laboratory Southeast University under Grant 2021D03。
文摘In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)environments.In the proposed sum-of-sinusoids(SoS)channel model,the waves that emerge from the transmitter undergo line-of-sight(LoS)and non-line-of-sight(NLoS)propagation to the receiver,which makes the model suitable for describing numerous V2X wireless communication scenarios for sixth-generation(6G).We derive expressions for the real and imaginary parts of the complex channel impulse response(CIR),which characterize the physical propagation characteristics of V2X wireless channels.The statistical properties of the real and imaginary parts of the complex CIRs,i.e.,autocorrelation functions(ACFs),Doppler power spectral densities(PSDs),cross-correlation functions(CCFs),and variances of ACFs and CCFs,are derived and discussed.Simulation results are generated and match those predicted by the underlying theory,demonstrating the accuracy of our derivation and analysis.The proposed framework and underlying theory arise as an efficient tool to investigate the statistical properties of 6G MIMO V2X communication systems.
基金L.Liang was supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20220810in part by the National Natural Science Foundation of China under Grant 62201145 and Grant 62231019S.Jin was supported in part by the National Natural Science Foundation of China(NSFC)under Grants 62261160576,62341107,61921004。
文摘In this paper,we investigate the problem of fast spectrum sharing in vehicle-to-everything com-munication.In order to improve the spectrum effi-ciency of the whole system,the spectrum of vehicle-to-infrastructure links is reused by vehicle-to-vehicle links.To this end,we model it as a problem of deep reinforcement learning and tackle it with prox-imal policy optimization.A considerable number of interactions are often required for training an agent with good performance,so simulation-based training is commonly used in communication networks.Nev-ertheless,severe performance degradation may occur when the agent is directly deployed in the real world,even though it can perform well on the simulator,due to the reality gap between the simulation and the real environments.To address this issue,we make prelim-inary efforts by proposing an algorithm based on meta reinforcement learning.This algorithm enables the agent to rapidly adapt to a new task with the knowl-edge extracted from similar tasks,leading to fewer in-teractions and less training time.Numerical results show that our method achieves near-optimal perfor-mance and exhibits rapid convergence.
