A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a tr...A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.展开更多
To handle the handover challenge in Express Train Access Networks(ETAN).mobility fading effects in high speed railway environments should be addressed first.Based on the investigation of fading effects in this paper,w...To handle the handover challenge in Express Train Access Networks(ETAN).mobility fading effects in high speed railway environments should be addressed first.Based on the investigation of fading effects in this paper,we obtain two theoretical bounds:HOTiming upper bound and HO-Margin lower bound,which are helpful guidelines to study the handover challenge today and in the future.Then,we apply them to analyze performance of conventional handover technologies and our proposal in ETAN.This follow-up theory analyses and simulation experiment results demonstrate that the proposed handover solution can minimize handover time up to 4ms(which is the fastest one so far),and reduce HO-Margin to 0.16 dB at a train speed of 350km/h.展开更多
针对车-车(vehicle to rechicle,V2V)通信系统对无线电信道衰落特性和模型的需求,分别在两种隧道场景中5.9 GHz和5.2 GHz频段下进行了V2V无线电信道测量活动,并对隧道外、隧道内和两者之间的连接部分场景进行了小尺度衰落特性分析.基于...针对车-车(vehicle to rechicle,V2V)通信系统对无线电信道衰落特性和模型的需求,分别在两种隧道场景中5.9 GHz和5.2 GHz频段下进行了V2V无线电信道测量活动,并对隧道外、隧道内和两者之间的连接部分场景进行了小尺度衰落特性分析.基于近距离(close-in,CI)对数模型和ABG(α-β-γ)模型建立了基于距离的接收功率模型,对两种场景隧道内外的接收功率进行了评估和比较,路径损耗指数分别为1.83和1.9,结果表明参考距离为1 m的CI对数模型具有更高的拟合度.此外,将测量数据幅度的衰落分布与五种典型的理论衰落分布进行比较分析,发现其特征更接近于具有最小拟合优度值的莱斯分布,且隧道内的莱斯K因子小于隧道外.同时,给出了隧道内和隧道外之间连接处基于距离的莱斯K因子模型,发现连接处的K因子与距离无关,而隧道内的K因子随距离增大而减小.展开更多
射频识别(RFID,radio frequency identification)技术自被提出以来,因其便捷高效的独特优势逐渐被广泛应用于交通、物流、工业和商业等领域。RFID标签作为存储可识别数据的载体,在RFID系统中具有至关重要的作用,越来越多的功能与模块被...射频识别(RFID,radio frequency identification)技术自被提出以来,因其便捷高效的独特优势逐渐被广泛应用于交通、物流、工业和商业等领域。RFID标签作为存储可识别数据的载体,在RFID系统中具有至关重要的作用,越来越多的功能与模块被嵌入RFID标签,并发展成为不同应用领域的智能标签。近年来,随着物联网和各种新型反向散射技术的发展,无线无源智能标签逐渐兴起。无线无源智能标签应用无源反向散射技术,借助射频信号获取能量并传输信息。从RFID技术入手,简要介绍了RFID的发展历史和传统智能标签,比较了传统智能标签和无线无源智能标签的区别,总结了无线无源智能标签的优点,列举了其在不同领域的具体应用,并分析了当前面临的挑战性问题。展开更多
The fifth generation(5G)communication has been a hotspot of research in recent years,and both research institutions and industrial enterprises put a lot of interests in 5G communications at some new frequency bands.In...The fifth generation(5G)communication has been a hotspot of research in recent years,and both research institutions and industrial enterprises put a lot of interests in 5G communications at some new frequency bands.In this paper,we investigate the radio channels of 5G systems below 6 GHz according to the 5G communication requirements and scenarios.Channel measurements were conducted on the campus of Beijing Jiaotong University,China at two key optional frequency bands below 6 GHz.By using the measured data,we analyzed key channel parameters at 460 MHz and 3.5 GHz,such as power delay profile,path loss exponent,shadow fading,and delay spread.The results are helpful for the 5G communication system design.展开更多
基金supported by the National Key R&D Program of China under Grant 2021YFB1407001the National Natural Science Foundation of China (NSFC) under Grants 62001269 and 61960206006+2 种基金the State Key Laboratory of Rail Traffic Control and Safety (under Grants RCS2022K009)Beijing Jiaotong University, the Future Plan Program for Young Scholars of Shandong Universitythe EU H2020 RISE TESTBED2 project under Grant 872172
文摘A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.
基金supported by the National Basic Research Program of China (973 Program)(No.2012CB315606 and 2010CB328201)
文摘To handle the handover challenge in Express Train Access Networks(ETAN).mobility fading effects in high speed railway environments should be addressed first.Based on the investigation of fading effects in this paper,we obtain two theoretical bounds:HOTiming upper bound and HO-Margin lower bound,which are helpful guidelines to study the handover challenge today and in the future.Then,we apply them to analyze performance of conventional handover technologies and our proposal in ETAN.This follow-up theory analyses and simulation experiment results demonstrate that the proposed handover solution can minimize handover time up to 4ms(which is the fastest one so far),and reduce HO-Margin to 0.16 dB at a train speed of 350km/h.
文摘射频识别(RFID,radio frequency identification)技术自被提出以来,因其便捷高效的独特优势逐渐被广泛应用于交通、物流、工业和商业等领域。RFID标签作为存储可识别数据的载体,在RFID系统中具有至关重要的作用,越来越多的功能与模块被嵌入RFID标签,并发展成为不同应用领域的智能标签。近年来,随着物联网和各种新型反向散射技术的发展,无线无源智能标签逐渐兴起。无线无源智能标签应用无源反向散射技术,借助射频信号获取能量并传输信息。从RFID技术入手,简要介绍了RFID的发展历史和传统智能标签,比较了传统智能标签和无线无源智能标签的区别,总结了无线无源智能标签的优点,列举了其在不同领域的具体应用,并分析了当前面临的挑战性问题。
基金supported by the National Natural Science Foundation of China under Grant 61501020the State Key Laboratory of Rail Traffic Control and Safety under Grant RCS2016ZJ005+5 种基金the China Postdoctoral Science Foundation under Grant 2016M591355the Fundamental Research Funds for the Central Universities(No.2016JBZ006)the Special Project of Cultivation and Development of Science and Technology Innovation Base in 2015the National Natural Science Foundation of China under Grant U1334202the Natural Science Base Research Plan in Shanxi Province of China under Grant 2015JM6320the Key Project from Beijing science and Technology Commission under Grant D151100000115004.
文摘The fifth generation(5G)communication has been a hotspot of research in recent years,and both research institutions and industrial enterprises put a lot of interests in 5G communications at some new frequency bands.In this paper,we investigate the radio channels of 5G systems below 6 GHz according to the 5G communication requirements and scenarios.Channel measurements were conducted on the campus of Beijing Jiaotong University,China at two key optional frequency bands below 6 GHz.By using the measured data,we analyzed key channel parameters at 460 MHz and 3.5 GHz,such as power delay profile,path loss exponent,shadow fading,and delay spread.The results are helpful for the 5G communication system design.
