An integrated sensing and communication(ISAC)scheme for a millimeter wave(mmWave)multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)Vehicle-to-Infrastructure(V2I)system is presented,in...An integrated sensing and communication(ISAC)scheme for a millimeter wave(mmWave)multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)Vehicle-to-Infrastructure(V2I)system is presented,in which both the access point(AP)and the vehicle are equipped with large antenna arrays and employ hybrid analog and digital beamforming structures to compensate the path loss,meanwhile compromise between hardware complexity and system performance.Based on the sparse scattering nature of the mmWave channel,the received signal at the AP is organized to a four-order tensor by the introduced novel frame structure.A CANDECOMP/PARAFAC(CP)decomposition-based method is proposed for time-varying channel parameter extraction,including angles of departure/arrival(AoDs/AoAs),Doppler shift,time delay and path gain.Then leveraging the estimates of channel parameters,a nonlinear weighted least-square problem is proposed to recover the location accurately,heading and velocity of vehicles.Simulation results show that the proposed methods are effective and efficient in time-varying channel estimation and vehicle sensing in mmWave MIMOOFDM V2I systems.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An integrated sensing and communication(ISAC)scheme for a millimeter wave(mmWave)multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)Vehicle-to-Infrastructure(V2I)system is presented,in which both the access point(AP)and the vehicle are equipped with large antenna arrays and employ hybrid analog and digital beamforming structures to compensate the path loss,meanwhile compromise between hardware complexity and system performance.Based on the sparse scattering nature of the mmWave channel,the received signal at the AP is organized to a four-order tensor by the introduced novel frame structure.A CANDECOMP/PARAFAC(CP)decomposition-based method is proposed for time-varying channel parameter extraction,including angles of departure/arrival(AoDs/AoAs),Doppler shift,time delay and path gain.Then leveraging the estimates of channel parameters,a nonlinear weighted least-square problem is proposed to recover the location accurately,heading and velocity of vehicles.Simulation results show that the proposed methods are effective and efficient in time-varying channel estimation and vehicle sensing in mmWave MIMOOFDM V2I systems.
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。
