无线信道建模对于理解、设计和优化无线通信系统具有重要意义,是无线通信领域中不可或缺的一部分。为了满足车联网(vehicle to everything,V2X)环境中的通信需求,研究空间中障碍物的分布对信道衰落特性的影响,本文提出了一种新的随机散...无线信道建模对于理解、设计和优化无线通信系统具有重要意义,是无线通信领域中不可或缺的一部分。为了满足车联网(vehicle to everything,V2X)环境中的通信需求,研究空间中障碍物的分布对信道衰落特性的影响,本文提出了一种新的随机散射簇生成算法,即通过把Matérn硬核点过程和泊松簇过程相结合来模拟真实V2X信道中的障碍物。在算法中,依据真实环境障碍物的方位设置散射簇的坐标位置,根据周围障碍物密度合理设置簇内散射点数量。利用传播图论进行仿真,考虑直射路径和单跳散射路径,基于信道冲激响应(channel impulse response,CIR)分别研究了功率延迟分布(power delay profile,PDP)和多普勒功率谱密度(Doppler power spectrum density,DPSD),并分析了不同移动轨迹下的均方根(root mean square,RMS)时延扩展的累计分布函数(cumulative distribution function,CDF),以及莱斯K因子的分布特性和角度功率谱(power angular spectrum,PAS)的分布。本文研究验证得到,所提出的模型有助于分析车辆-基础设施(vehicle to infrastructure,V2I)通信场景下的时域非平稳特性,为V2X通信系统的设计和优化提供了重要参考。展开更多
The Global Position System(GPS)is a reliable method for positioning in most scenarios,but it falls short in harsh environments like urban vehicular scenarios,where numerous trees or flyovers obstruct the signals.This ...The Global Position System(GPS)is a reliable method for positioning in most scenarios,but it falls short in harsh environments like urban vehicular scenarios,where numerous trees or flyovers obstruct the signals.This presents an unprecedented challenge for autonomous vehicles or applications requiring high accuracy.Fortunately,vehicular ad-hoc networks(VANET)offer an effective solution,where vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications are used to enhance location awareness.In V2I communications,the roadside units(RSU)transmit beacon packets,and the vehicle receives numerous packets from different RSUs to establish communication.To further improve localization accuracy,a cross-covariance matrices-alternating least square(CCM-ALS)algorithm is proposed.The algorithm relies on ALS of the CCM for obtaining the position of vehicles in V2I communications.The algorithm is highly precise compared to traditional angle of arrival(AOA)positioning and not inferior to direct position determination(DPD)approaches while being low in complexity,which is crucial for moving vehicles.The numerical results verify the superiority of the proposed method.展开更多
车联网对于超高可靠与低时延通信(Ultra-Reliable and Low Latency Communications,URLLC)具有严格的要求,特别对于车到基础设施(Vehicle to Infrastructure,V2I)场景,URLLC对传输管理交通状况至关重要.3GPP Cellular-V2X(C-V2X)作为现...车联网对于超高可靠与低时延通信(Ultra-Reliable and Low Latency Communications,URLLC)具有严格的要求,特别对于车到基础设施(Vehicle to Infrastructure,V2I)场景,URLLC对传输管理交通状况至关重要.3GPP Cellular-V2X(C-V2X)作为现在支撑车联网URLLC主流的无线技术,仍存在技术挑战.为进一步提升通信性能,本文在V2I场景下,基于车载终端、路侧单元(Road Side Unit,RSU)与边缘计算车联网服务器(Internet of Vehicles Server,IoV Server)的交互,设计了一种基于C-V2I规范的智能信道估计框架.在IoV Server中,本文提出了一种基于深度学习的信道估计算法,该算法利用一维卷积神经网络(One Dimensional Convolution Neural Network,1D CNN)完成频域插值和条件循环单元(Conditional Recurrent Unit,CRU)进行时域状态预测,通过引入额外的速度编码矢量和多径编码矢量跟踪环境的变化,对不同移动环境下的信道数据进行精确训练.最后通过系统仿真与分析表明,所提算法能够通过信道参数编码追踪不同高速移动环境下的信道变化,实现对信道数据的精确训练.与车联网代表性信道估计算法相比,所提算法提升了信道估计精度,降低了误码率和增强了鲁棒性.展开更多
In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving...In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges.V2I(Vehicle-to-Infrastructure)communication is a potential solution to enable cooperative intelligence of vehicles and roads.In this paper,the RGB-PVRCNN,an environment perception framework,is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication technology.This framework integrates vision feature based on PVRCNN.The normal distributions transform(NDT)point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the intersection.The field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.展开更多
Connected and autonomous vehicle(CAV)vehicle to infrastructure(V2I)scenarios have more stringent requirements on the communication rate,delay,and reliability of the Internet of vehicles(Io V).New radio vehicle to ever...Connected and autonomous vehicle(CAV)vehicle to infrastructure(V2I)scenarios have more stringent requirements on the communication rate,delay,and reliability of the Internet of vehicles(Io V).New radio vehicle to everything(NR-V2X)adopts link adaptation(LA)to improve the efficiency and reliability of road safety information transmission.In order to solve the problem that the existing LA scheduling algorithms cannot adapt to the Doppler shift and complex fast time-varying channel in V2I scenario,resulting in low reliability of information transmission,this paper proposes a deep Q-learning(DQL)-based massive multiple-input multiple-output(MIMO)LA scheduling algorithm for autonomous driving V2I scenario.The algorithm combines deep neural network(DNN)with Q-learning(QL)algorithm,which is used for joint scheduling of modulation and coding scheme(MCS)and space division multiplexing(SDM).The system simulation results show that the algorithm proposed in this paper can fully adapt to the different channel environment in the V2I scenario,and select the optimal MCS and SDM for the transmission of road safety information,thereby the accuracy of road safety information transmission is improved,collision accidents can be avoided,and bring a good autonomous driving experience.展开更多
The paper investigates a few of the major areas of the next generation technological advancement,“smart city planning concept”.The areas that the paper focuses are vehicle to grid(V2G),sun to vehicle(S2V),and vehicl...The paper investigates a few of the major areas of the next generation technological advancement,“smart city planning concept”.The areas that the paper focuses are vehicle to grid(V2G),sun to vehicle(S2V),and vehicle to infrastructure(V2I).For the bi-directional crowd energy single entity concept,V2G and building to grid(B2G)are the primary parts of distributed renewable generation(DRG)under smart living.This research includes an in-depth overview of this three major areas.Next,the research conducts a case analysis of V2G,S2V,and V2I along with their possible limitations in order to find out the novel solutions for future development both for academia and industry levels.Lastly,few possible solutions have been proposed to minimize the limitations and to develop the existing system for future expansion.展开更多
在应急通信中,无人机的应用越来越广泛,提出了一种无人机辅助的V2I(vehicle to infrastructure)车载通信技术。由于通信距离、地形遮挡等原因,地面的车辆与基站可能无法实现直接通信,此时无人机可被用作通信中继。在通信过程中,为了满...在应急通信中,无人机的应用越来越广泛,提出了一种无人机辅助的V2I(vehicle to infrastructure)车载通信技术。由于通信距离、地形遮挡等原因,地面的车辆与基站可能无法实现直接通信,此时无人机可被用作通信中继。在通信过程中,为了满足地面车辆通信的公平性需求,基于地面移动车辆的位置坐标,选定一个小于相干时间的时间间隔,计算车辆在每个时间间隔的传输速率,并将最大化最小传输速率作为优化目标。为解决此问题,提出将整个通信过程离散化,先计算在每个时间间隔范围内使最小传输速率最大化的无人机飞行轨迹,从而得到整个通信过程的无人机飞行路径。仿真表明,通过对无人机进行路径规划,车辆的信息传输速率明显优于无人机位置固定情况下的传输速率。展开更多
文摘无线信道建模对于理解、设计和优化无线通信系统具有重要意义,是无线通信领域中不可或缺的一部分。为了满足车联网(vehicle to everything,V2X)环境中的通信需求,研究空间中障碍物的分布对信道衰落特性的影响,本文提出了一种新的随机散射簇生成算法,即通过把Matérn硬核点过程和泊松簇过程相结合来模拟真实V2X信道中的障碍物。在算法中,依据真实环境障碍物的方位设置散射簇的坐标位置,根据周围障碍物密度合理设置簇内散射点数量。利用传播图论进行仿真,考虑直射路径和单跳散射路径,基于信道冲激响应(channel impulse response,CIR)分别研究了功率延迟分布(power delay profile,PDP)和多普勒功率谱密度(Doppler power spectrum density,DPSD),并分析了不同移动轨迹下的均方根(root mean square,RMS)时延扩展的累计分布函数(cumulative distribution function,CDF),以及莱斯K因子的分布特性和角度功率谱(power angular spectrum,PAS)的分布。本文研究验证得到,所提出的模型有助于分析车辆-基础设施(vehicle to infrastructure,V2I)通信场景下的时域非平稳特性,为V2X通信系统的设计和优化提供了重要参考。
基金supported by the National Natural Science Foundation of China(62371225).
文摘The Global Position System(GPS)is a reliable method for positioning in most scenarios,but it falls short in harsh environments like urban vehicular scenarios,where numerous trees or flyovers obstruct the signals.This presents an unprecedented challenge for autonomous vehicles or applications requiring high accuracy.Fortunately,vehicular ad-hoc networks(VANET)offer an effective solution,where vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications are used to enhance location awareness.In V2I communications,the roadside units(RSU)transmit beacon packets,and the vehicle receives numerous packets from different RSUs to establish communication.To further improve localization accuracy,a cross-covariance matrices-alternating least square(CCM-ALS)algorithm is proposed.The algorithm relies on ALS of the CCM for obtaining the position of vehicles in V2I communications.The algorithm is highly precise compared to traditional angle of arrival(AOA)positioning and not inferior to direct position determination(DPD)approaches while being low in complexity,which is crucial for moving vehicles.The numerical results verify the superiority of the proposed method.
基金This research was supported by the National Key Research and Development Program of China under Grant No.2017YFB0102502the Beijing Municipal Natural Science Foundation No.L191001+2 种基金the National Natural Science Foundation of China under Grant No.61672082 and 61822101the Newton Advanced Fellowship under Grant No.62061130221the Young Elite Scientists Sponsorship Program by Hunan Provincial Department of Education under Grant No.18B142.
文摘In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges.V2I(Vehicle-to-Infrastructure)communication is a potential solution to enable cooperative intelligence of vehicles and roads.In this paper,the RGB-PVRCNN,an environment perception framework,is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication technology.This framework integrates vision feature based on PVRCNN.The normal distributions transform(NDT)point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the intersection.The field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.
基金supported by the Natural Science Foundation of Chongqing(No.cstc2019jcyjmsxmX0017)。
文摘Connected and autonomous vehicle(CAV)vehicle to infrastructure(V2I)scenarios have more stringent requirements on the communication rate,delay,and reliability of the Internet of vehicles(Io V).New radio vehicle to everything(NR-V2X)adopts link adaptation(LA)to improve the efficiency and reliability of road safety information transmission.In order to solve the problem that the existing LA scheduling algorithms cannot adapt to the Doppler shift and complex fast time-varying channel in V2I scenario,resulting in low reliability of information transmission,this paper proposes a deep Q-learning(DQL)-based massive multiple-input multiple-output(MIMO)LA scheduling algorithm for autonomous driving V2I scenario.The algorithm combines deep neural network(DNN)with Q-learning(QL)algorithm,which is used for joint scheduling of modulation and coding scheme(MCS)and space division multiplexing(SDM).The system simulation results show that the algorithm proposed in this paper can fully adapt to the different channel environment in the V2I scenario,and select the optimal MCS and SDM for the transmission of road safety information,thereby the accuracy of road safety information transmission is improved,collision accidents can be avoided,and bring a good autonomous driving experience.
文摘The paper investigates a few of the major areas of the next generation technological advancement,“smart city planning concept”.The areas that the paper focuses are vehicle to grid(V2G),sun to vehicle(S2V),and vehicle to infrastructure(V2I).For the bi-directional crowd energy single entity concept,V2G and building to grid(B2G)are the primary parts of distributed renewable generation(DRG)under smart living.This research includes an in-depth overview of this three major areas.Next,the research conducts a case analysis of V2G,S2V,and V2I along with their possible limitations in order to find out the novel solutions for future development both for academia and industry levels.Lastly,few possible solutions have been proposed to minimize the limitations and to develop the existing system for future expansion.
文摘在应急通信中,无人机的应用越来越广泛,提出了一种无人机辅助的V2I(vehicle to infrastructure)车载通信技术。由于通信距离、地形遮挡等原因,地面的车辆与基站可能无法实现直接通信,此时无人机可被用作通信中继。在通信过程中,为了满足地面车辆通信的公平性需求,基于地面移动车辆的位置坐标,选定一个小于相干时间的时间间隔,计算车辆在每个时间间隔的传输速率,并将最大化最小传输速率作为优化目标。为解决此问题,提出将整个通信过程离散化,先计算在每个时间间隔范围内使最小传输速率最大化的无人机飞行轨迹,从而得到整个通信过程的无人机飞行路径。仿真表明,通过对无人机进行路径规划,车辆的信息传输速率明显优于无人机位置固定情况下的传输速率。
