Low-earth-orbit(LEO)satellite network has become a critical component of the satelliteterrestrial integrated network(STIN)due to its superior signal quality and minimal communication latency.However,the highly dynamic...Low-earth-orbit(LEO)satellite network has become a critical component of the satelliteterrestrial integrated network(STIN)due to its superior signal quality and minimal communication latency.However,the highly dynamic nature of LEO satellites leads to limited and rapidly varying contact time between them and Earth stations(ESs),making it difficult to timely download massive communication and remote sensing data within the limited time window.To address this challenge in heterogeneous satellite networks with coexisting geostationary-earth-orbit(GEO)and LEO satellites,this paper proposes a dynamic collaborative inter-satellite data download strategy to optimize the long-term weighted energy consumption and data downloads within the constraints of on-board power,backlog stability and time-varying contact.Specifically,the Lyapunov optimization theory is applied to transform the long-term stochastic optimization problem,subject to time-varying contact time and on-board power constraints,into multiple deterministic single time slot problems,based on which online distributed algorithms are developed to enable each satellite to independently obtain the transmit power allocation and data processing decisions in closed-form.Finally,the simulation results demonstrate the superiority of the proposed scheme over benchmarks,e.g.,achieving asymptotic optimality of the weighted energy consumption and data downloads,while maintaining stability of the on-board backlog.展开更多
Communication is important for providing intelligent services in connected vehicles.Vehicles must be able to communicate with different places and exchange information while driving.For service operation,connected veh...Communication is important for providing intelligent services in connected vehicles.Vehicles must be able to communicate with different places and exchange information while driving.For service operation,connected vehicles frequently attempt to download large amounts of data.They can request data downloading to a road side unit(RSU),which provides infrastructure for connected vehicles.The RSU is a data bottleneck in a transportation system because data traffic is concentrated on the RSU.Therefore,it is not appropriate for a connected vehicle to always attempt a high speed download from the RSU.If the mobile network between a connected vehicle and an RSU has poor connection quality,the efficiency and speed of the data download from the RSU is decreased.This problem affects the quality of the user experience.Therefore,it is important for a connected vehicle to connect to an RSU with consideration of the network conditions in order to try to maximize download speed.The proposed method maximizes download speed from an RSU using a machine learning algorithm.To collect and learn from network data,fog computing is used.A fog server is integrated with the RSU to perform computing.If the algorithm recognizes that conditions are not good for mass data download,it will not attempt to download at high speed.Thus,the proposed method can improve the efficiency of high speed downloads.This conclusion was validated using extensive computer simulations.展开更多
The recent advances in wireless communication technology enable high-speed vehicles to download data from roadside units(RSUs). However, the data download volume of individual vehicle is rather restricted due to high ...The recent advances in wireless communication technology enable high-speed vehicles to download data from roadside units(RSUs). However, the data download volume of individual vehicle is rather restricted due to high mobility and limited transmission range of vehicles, bringing users poor performance. To address this issue, we exploit the combination of both clustering and carry-and-forward schemes in this paper. Our scheme coordinates the cooperation of multiple infrastructures, cluster formation in the same direction and data forwarding of reverse vehicles to facilitate the target vehicle to download large-size content in dark areas. The process of data dissemination and achievable data download volume are then derived and analyzed theoretically. Finally, we conduct extensive simulations to verify the performance of the proposed scheme. Results show significant benefits of the proposed scheme in terms of increasing data download volume and throughput.展开更多
介绍了 A DI公司的 MCS5 1系列单片机的兼容芯片 A DμC812 ,并基于该芯片设计了一种具有在系统可编程能力的数据采集电路。该数据采集电路采用 A DμC812的片内 A / D和 D/ A转换器减小了电路体积 ;同时应用这种芯片的在系统可编程能...介绍了 A DI公司的 MCS5 1系列单片机的兼容芯片 A DμC812 ,并基于该芯片设计了一种具有在系统可编程能力的数据采集电路。该数据采集电路采用 A DμC812的片内 A / D和 D/ A转换器减小了电路体积 ;同时应用这种芯片的在系统可编程能力不仅可以方便地在应用现场对系统进行升级 ,而且在设计调试阶段不需要专用硬件开发设备和编程设备的支持。展开更多
基金supported by the National Natural Science Foundation of China under Grant 62371098the National Key Laboratory ofWireless Communications Foundation under Grant IFN20230203the National Key Research and Development Program of China under Grant 2021YFB2900404.
文摘Low-earth-orbit(LEO)satellite network has become a critical component of the satelliteterrestrial integrated network(STIN)due to its superior signal quality and minimal communication latency.However,the highly dynamic nature of LEO satellites leads to limited and rapidly varying contact time between them and Earth stations(ESs),making it difficult to timely download massive communication and remote sensing data within the limited time window.To address this challenge in heterogeneous satellite networks with coexisting geostationary-earth-orbit(GEO)and LEO satellites,this paper proposes a dynamic collaborative inter-satellite data download strategy to optimize the long-term weighted energy consumption and data downloads within the constraints of on-board power,backlog stability and time-varying contact.Specifically,the Lyapunov optimization theory is applied to transform the long-term stochastic optimization problem,subject to time-varying contact time and on-board power constraints,into multiple deterministic single time slot problems,based on which online distributed algorithms are developed to enable each satellite to independently obtain the transmit power allocation and data processing decisions in closed-form.Finally,the simulation results demonstrate the superiority of the proposed scheme over benchmarks,e.g.,achieving asymptotic optimality of the weighted energy consumption and data downloads,while maintaining stability of the on-board backlog.
文摘Communication is important for providing intelligent services in connected vehicles.Vehicles must be able to communicate with different places and exchange information while driving.For service operation,connected vehicles frequently attempt to download large amounts of data.They can request data downloading to a road side unit(RSU),which provides infrastructure for connected vehicles.The RSU is a data bottleneck in a transportation system because data traffic is concentrated on the RSU.Therefore,it is not appropriate for a connected vehicle to always attempt a high speed download from the RSU.If the mobile network between a connected vehicle and an RSU has poor connection quality,the efficiency and speed of the data download from the RSU is decreased.This problem affects the quality of the user experience.Therefore,it is important for a connected vehicle to connect to an RSU with consideration of the network conditions in order to try to maximize download speed.The proposed method maximizes download speed from an RSU using a machine learning algorithm.To collect and learn from network data,fog computing is used.A fog server is integrated with the RSU to perform computing.If the algorithm recognizes that conditions are not good for mass data download,it will not attempt to download at high speed.Thus,the proposed method can improve the efficiency of high speed downloads.This conclusion was validated using extensive computer simulations.
基金supported by the National Natural Science Foundation of China under Grant No.61571350Key Research and Development Program of Shaanxi(Contract No.2017KW-004,2017ZDXM-GY-022)the 111 Project(B08038)
文摘The recent advances in wireless communication technology enable high-speed vehicles to download data from roadside units(RSUs). However, the data download volume of individual vehicle is rather restricted due to high mobility and limited transmission range of vehicles, bringing users poor performance. To address this issue, we exploit the combination of both clustering and carry-and-forward schemes in this paper. Our scheme coordinates the cooperation of multiple infrastructures, cluster formation in the same direction and data forwarding of reverse vehicles to facilitate the target vehicle to download large-size content in dark areas. The process of data dissemination and achievable data download volume are then derived and analyzed theoretically. Finally, we conduct extensive simulations to verify the performance of the proposed scheme. Results show significant benefits of the proposed scheme in terms of increasing data download volume and throughput.
文摘介绍了 A DI公司的 MCS5 1系列单片机的兼容芯片 A DμC812 ,并基于该芯片设计了一种具有在系统可编程能力的数据采集电路。该数据采集电路采用 A DμC812的片内 A / D和 D/ A转换器减小了电路体积 ;同时应用这种芯片的在系统可编程能力不仅可以方便地在应用现场对系统进行升级 ,而且在设计调试阶段不需要专用硬件开发设备和编程设备的支持。
