通信感知一体化(integrated sensing and communication,ISAC)通过实现通信和雷达感知的资源共享,在人机交互、车联网(vehicle-to-everything,V2X)、遥感和环境检测等应用场景中很有前景。为解决高移动性的车联网中通信和雷达感知所需...通信感知一体化(integrated sensing and communication,ISAC)通过实现通信和雷达感知的资源共享,在人机交互、车联网(vehicle-to-everything,V2X)、遥感和环境检测等应用场景中很有前景。为解决高移动性的车联网中通信和雷达感知所需的频率资源的冲突,基于正交时频空间(orthogonal time frequency space,OTFS)调制提出了OTFS-ISAC系统,基站通过接收车辆反射的回波,获得车辆运动参数,从而建立车辆运动的移动拓扑模型。此外,针对多车辆移动场景,提出了保证用户公平性的非正交多址接入OTFS-ISAC系统设计和功率分配算法。仿真结果表明,相比于无雷达感知辅助的非正交多址OTFS系统,基于OTFS-ISAC的非正交多址接入系统实现了25%的信息速率提升。展开更多
The rise of the Internet of Things and autonomous systems has made connecting vehicles more critical.Connected autonomous vehicles can create diverse communication networks that can improve the environment and offer c...The rise of the Internet of Things and autonomous systems has made connecting vehicles more critical.Connected autonomous vehicles can create diverse communication networks that can improve the environment and offer contemporary applications.With the advent of Fifth Generation(5G)networks,vehicle-to-everything(V2X)networks are expected to be highly intelligent,reside on superfast,reliable,and low-latency connections.Network slicing,machine learning(ML),and deep learning(DL)are related to network automation and optimization in V2X communication.ML/DL with network slicing aims to optimize the performance,reliability of the V2X networks,personalized services,costs,and scalability,and thus,it enhances the overall driving experience.These advantages can ultimately lead to a safer and more efficient transportation system.However,existing long-term evolution systems and enabling 5G technologies cannot meet such dynamic requirements without adding higher complexity levels.ML algorithms mitigate complexity levels,which can be highly instrumental in such vehicular communication systems.This study aims to review V2X slicing based on a proposed taxonomy that describes the enablers of slicing,a different configuration of slicing,the requirements of slicing,and the ML algorithm used to control and manage to slice.This study also reviews various research works established in network slicing through ML algorithms to enable V2X communication use cases,focusing on V2X network slicing and considering efficient control and management.The enabler technologies are considered in light of the network requirements,particular configurations,and the underlying methods and algorithms,with a review of some critical challenges and possible solutions available.The paper concludes with a future roadmap by discussing some open research issues and future directions.展开更多
This paper proposes a novel optimization scheme to support stable and reliable vehicle-to-everything connections in two-tier networks,where the uplink channel of the cellular user is reused by underlay vehicle-to-vehi...This paper proposes a novel optimization scheme to support stable and reliable vehicle-to-everything connections in two-tier networks,where the uplink channel of the cellular user is reused by underlay vehicle-to-vehicle communications.However,considering complex channel fading and high-speed vehicle movement,the cer-tainty assumption is impractical and fails to maintain power control strategy in reality in the traditional statical vehicular networks.Rather than the perfect channel state information assumption,the first-order Gauss-Markov process which is a probabilistic model affected by vehicle speed and fading is introduced to describe imperfect channel gains.Moreover,interference management is a major challenge in reusing communications,especially in uncertain channel environments.Power control is an effective way to realize interference management,and optimal power allocation can ensure that interference of the user meets the communication requirements.In this study,the sum-rate-oriented power control scheme and minimum-rate-oriented power control scheme were implemented to manage interference and satisfy different design objectives.Since both of these schemes are non-convex and intractable,the Bernstein approximation and successive convex approximation methods were adopted to transform the original problems into convex ones.Furthermore,a novel distributed robust power control al-gorithm was developed to determine the optimal solutions.The performance of the algorithm was evaluated through numerical simulations,and the results indicate that the proposed algorithm is effective in vehicular communication networks with uncertain channel environments.展开更多
In this paper,we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles(HEVs)on a road with slope.Moreover,it is assumed that the targeted HEVs are in the conne...In this paper,we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles(HEVs)on a road with slope.Moreover,it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything(V2X),including geographic information,vehicle-to-infrastructure(V2I)information and vehicle-to-vehicle(V2V)information.The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator,where the running route is generated based on real-world data with slope and intersection position.The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time.To show the HEV powertrain characteristics,a case study is given with the speed planning and energy management strategy.展开更多
The fifth-generation(5G)wireless communication networks are expected to play an essential role in the transformation of vertical industries.Among many exciting applications to be enabled by 5G,logistics tasks in indus...The fifth-generation(5G)wireless communication networks are expected to play an essential role in the transformation of vertical industries.Among many exciting applications to be enabled by 5G,logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything(V2X)communications.In this paper,a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks.The proposed framework includes three layers:a perception and execution layer,a logistics layer,and a configuration layer.In addition to the collaboration among these three layers,this study addresses the collaboration among devices,edge servers,and cloud services.For effective logistics in industrial parks,task collaboration is achieved through four functions:environmental perception and map construction,task allocation,path planning,and vehicle movement.To dynamically coordinate these functions,device–edge–cloud collaboration,which is supported by 5G slices and V2X communication technology,is applied.Then,the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks.Finally,a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.展开更多
This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles(HEVs)on a road with a slope.We assume that HEVs are in a connecte...This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles(HEVs)on a road with a slope.We assume that HEVs are in a connected environment with real-time vehicle-to-everything information,including geographic information,vehicle-to-infrastructure information and vehicle-to-vehicle information.The benchmark problem to be solved is based on HEV powertrain control using traffic information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time.The proposed strategy includes multiple rules and model predictive control(MPC).The rules of this strategy are designed based on external environment information to maintain safe driving and to determine the driving mode.To improve fuel economy,the optimal energy management strategy is primarily considered,and to perform real-time energy management via RHC-based optimization in a connected environment with safety constraints,a key issue is to predict the dynamics of the preceding vehicle during the targeted horizon.Therefore,this paper presents a real-time model-based optimization strategy with learning-based prediction of the vehicle’s future speed.To validate the proposed optimization strategy,a powertrain control simulation platform in a traffic-in-the-loop environment is constructed,and case study results performed on the constructed platform are reported and discussed.展开更多
文摘通信感知一体化(integrated sensing and communication,ISAC)通过实现通信和雷达感知的资源共享,在人机交互、车联网(vehicle-to-everything,V2X)、遥感和环境检测等应用场景中很有前景。为解决高移动性的车联网中通信和雷达感知所需的频率资源的冲突,基于正交时频空间(orthogonal time frequency space,OTFS)调制提出了OTFS-ISAC系统,基站通过接收车辆反射的回波,获得车辆运动参数,从而建立车辆运动的移动拓扑模型。此外,针对多车辆移动场景,提出了保证用户公平性的非正交多址接入OTFS-ISAC系统设计和功率分配算法。仿真结果表明,相比于无雷达感知辅助的非正交多址OTFS系统,基于OTFS-ISAC的非正交多址接入系统实现了25%的信息速率提升。
基金This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB1807900the National Natural Science Foundation of China under Grant 62101306The work was also supported by Datang Linktester Technology Co.Ltd.
文摘The rise of the Internet of Things and autonomous systems has made connecting vehicles more critical.Connected autonomous vehicles can create diverse communication networks that can improve the environment and offer contemporary applications.With the advent of Fifth Generation(5G)networks,vehicle-to-everything(V2X)networks are expected to be highly intelligent,reside on superfast,reliable,and low-latency connections.Network slicing,machine learning(ML),and deep learning(DL)are related to network automation and optimization in V2X communication.ML/DL with network slicing aims to optimize the performance,reliability of the V2X networks,personalized services,costs,and scalability,and thus,it enhances the overall driving experience.These advantages can ultimately lead to a safer and more efficient transportation system.However,existing long-term evolution systems and enabling 5G technologies cannot meet such dynamic requirements without adding higher complexity levels.ML algorithms mitigate complexity levels,which can be highly instrumental in such vehicular communication systems.This study aims to review V2X slicing based on a proposed taxonomy that describes the enablers of slicing,a different configuration of slicing,the requirements of slicing,and the ML algorithm used to control and manage to slice.This study also reviews various research works established in network slicing through ML algorithms to enable V2X communication use cases,focusing on V2X network slicing and considering efficient control and management.The enabler technologies are considered in light of the network requirements,particular configurations,and the underlying methods and algorithms,with a review of some critical challenges and possible solutions available.The paper concludes with a future roadmap by discussing some open research issues and future directions.
基金supported by National Natural Science Foundation of China under grant 61873223,61803328the Natural Science Foundation of Hebei Province under grant F2019203095Beijing Natural Science Foundation under grant L201002.
文摘This paper proposes a novel optimization scheme to support stable and reliable vehicle-to-everything connections in two-tier networks,where the uplink channel of the cellular user is reused by underlay vehicle-to-vehicle communications.However,considering complex channel fading and high-speed vehicle movement,the cer-tainty assumption is impractical and fails to maintain power control strategy in reality in the traditional statical vehicular networks.Rather than the perfect channel state information assumption,the first-order Gauss-Markov process which is a probabilistic model affected by vehicle speed and fading is introduced to describe imperfect channel gains.Moreover,interference management is a major challenge in reusing communications,especially in uncertain channel environments.Power control is an effective way to realize interference management,and optimal power allocation can ensure that interference of the user meets the communication requirements.In this study,the sum-rate-oriented power control scheme and minimum-rate-oriented power control scheme were implemented to manage interference and satisfy different design objectives.Since both of these schemes are non-convex and intractable,the Bernstein approximation and successive convex approximation methods were adopted to transform the original problems into convex ones.Furthermore,a novel distributed robust power control al-gorithm was developed to determine the optimal solutions.The performance of the algorithm was evaluated through numerical simulations,and the results indicate that the proposed algorithm is effective in vehicular communication networks with uncertain channel environments.
文摘In this paper,we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles(HEVs)on a road with slope.Moreover,it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything(V2X),including geographic information,vehicle-to-infrastructure(V2I)information and vehicle-to-vehicle(V2V)information.The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator,where the running route is generated based on real-world data with slope and intersection position.The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time.To show the HEV powertrain characteristics,a case study is given with the speed planning and energy management strategy.
基金supported by the China National Key Research and Development Program(2018YFE0197700).
文摘The fifth-generation(5G)wireless communication networks are expected to play an essential role in the transformation of vertical industries.Among many exciting applications to be enabled by 5G,logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything(V2X)communications.In this paper,a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks.The proposed framework includes three layers:a perception and execution layer,a logistics layer,and a configuration layer.In addition to the collaboration among these three layers,this study addresses the collaboration among devices,edge servers,and cloud services.For effective logistics in industrial parks,task collaboration is achieved through four functions:environmental perception and map construction,task allocation,path planning,and vehicle movement.To dynamically coordinate these functions,device–edge–cloud collaboration,which is supported by 5G slices and V2X communication technology,is applied.Then,the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks.Finally,a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.
基金supported by the National Natural Science Foundation of China(No.61973053).The authors would like to thank the Toyota Motor Corporation for the technical support on this research work..
文摘This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles(HEVs)on a road with a slope.We assume that HEVs are in a connected environment with real-time vehicle-to-everything information,including geographic information,vehicle-to-infrastructure information and vehicle-to-vehicle information.The benchmark problem to be solved is based on HEV powertrain control using traffic information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time.The proposed strategy includes multiple rules and model predictive control(MPC).The rules of this strategy are designed based on external environment information to maintain safe driving and to determine the driving mode.To improve fuel economy,the optimal energy management strategy is primarily considered,and to perform real-time energy management via RHC-based optimization in a connected environment with safety constraints,a key issue is to predict the dynamics of the preceding vehicle during the targeted horizon.Therefore,this paper presents a real-time model-based optimization strategy with learning-based prediction of the vehicle’s future speed.To validate the proposed optimization strategy,a powertrain control simulation platform in a traffic-in-the-loop environment is constructed,and case study results performed on the constructed platform are reported and discussed.
