在第五代移动通信系统中,采用极化码编码下行控制信息,并通过物理下行控制信道(Physical Download Control Channel,PDCCH)发送给用户设备,用户设备通过极化码盲检获得属于自己的控制信息。在通常情况下,用户设备接收到的帧包括极化码...在第五代移动通信系统中,采用极化码编码下行控制信息,并通过物理下行控制信道(Physical Download Control Channel,PDCCH)发送给用户设备,用户设备通过极化码盲检获得属于自己的控制信息。在通常情况下,用户设备接收到的帧包括极化码字帧和噪声帧,若将其全部进行译码,则存在不必要的开销。针对此问题,提出了一种基于阈值检验的区分极化码字帧和噪声帧的方法。该方法根据收端接收到的极化码字各个节点呈现出不同的硬判可靠度,为各个节点设置不同的硬判错误率阈值,对所有帧按照未通过阈值检验的节点个数进行由小到大进行排序,选取较小的帧进入后续的译码盲检,从而在盲检前剔除一定数量的噪声帧,显著降低用户设备端的盲检译码能耗。仿真验证表明,在复杂度相同的情况下,与已有的区分算法相比,该方案能将漏检率降低90%以上。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
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
文摘在第五代移动通信系统中,采用极化码编码下行控制信息,并通过物理下行控制信道(Physical Download Control Channel,PDCCH)发送给用户设备,用户设备通过极化码盲检获得属于自己的控制信息。在通常情况下,用户设备接收到的帧包括极化码字帧和噪声帧,若将其全部进行译码,则存在不必要的开销。针对此问题,提出了一种基于阈值检验的区分极化码字帧和噪声帧的方法。该方法根据收端接收到的极化码字各个节点呈现出不同的硬判可靠度,为各个节点设置不同的硬判错误率阈值,对所有帧按照未通过阈值检验的节点个数进行由小到大进行排序,选取较小的帧进入后续的译码盲检,从而在盲检前剔除一定数量的噪声帧,显著降低用户设备端的盲检译码能耗。仿真验证表明,在复杂度相同的情况下,与已有的区分算法相比,该方案能将漏检率降低90%以上。
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.