LTE-U(LTE in unlicensed spectrum)是近来3GPP标准化组织讨论研究的用于缓解授权频段通信压力的新兴技术。首先对LTE-U技术进行概述,随后分析了LTE-U的工作频谱和设计要求,同时总结了其可能的部署场景和运行模式,然后对部署LTE-U存在...LTE-U(LTE in unlicensed spectrum)是近来3GPP标准化组织讨论研究的用于缓解授权频段通信压力的新兴技术。首先对LTE-U技术进行概述,随后分析了LTE-U的工作频谱和设计要求,同时总结了其可能的部署场景和运行模式,然后对部署LTE-U存在的难点和现有的应用解决方案的相关研究进行了详细阐述,最后对未来的研究方向进行了展望。展开更多
无线数据业务的快速增长给有限的频谱资源带来新的挑战。在当前的各种提高频谱效率方案中,LTE-U(LTE in unlicensed spectrum,LTE-U)通信系统获得了全球运营商的广泛认可。作为5G关键技术之一,LTE-U运用载波聚合技术(carrier aggregatio...无线数据业务的快速增长给有限的频谱资源带来新的挑战。在当前的各种提高频谱效率方案中,LTE-U(LTE in unlicensed spectrum,LTE-U)通信系统获得了全球运营商的广泛认可。作为5G关键技术之一,LTE-U运用载波聚合技术(carrier aggregation,CA)借助于非授权频段对数据业务进行分流,以达到提高网络数据传输速率、频谱利用率和增强用户移动性的目的。然而,由于LTE-U和WiFi系统接入技术的不同,如何解决两个系统之间的和谐共存成为LTE-U系统能否在非授权频段上使用的关键。对LTE-U的相关背景知识、工作模式、载波聚合技术、LTE-U设计要点进行介绍。指出当前LTE-U和WiFi在共存问题上面临的挑战,并对当前LTE-U和WiFi系统在非授权频段上共存的解决方案进行阐述分析和对比。对未来LTE-U和WiFi系统共存的研究方向进行了展望。展开更多
This paper proposes a deep learning(DL)resource allocation framework to achieve the harmonious coexistence between the transceiver pairs(TPs)and the Wi-Fi users in LTE-U networks.The nonconvex resource allocation is c...This paper proposes a deep learning(DL)resource allocation framework to achieve the harmonious coexistence between the transceiver pairs(TPs)and the Wi-Fi users in LTE-U networks.The nonconvex resource allocation is considered as a constrained learning problem and the deep neural network(DNN)is employed to approximate the optimal resource allocation decisions through unsupervised manner.A parallel DNN framework is proposed to deal with the two optimization variables in this problem,where one is the licensed power allocation unit and the other is the unlicensed time fraction occupied unit.Besides,to guarantee the feasibility of the proposed algorithm,the Lagrange dual method is used to relax the constraints into the DNN training process.Then,the dual variable and the DNN parameter are alternating update via the batch-based gradient decent method until the training process converges.Numerical results show that the proposed algorithm is feasible and has better performance than other general algorithms.展开更多
为了满足用户对高速率、低时延的网络需求,解决现网频谱资源稀缺的问题,介绍了一种非授权频段与授权频段载波聚合的方法,来提高频谱资源的利用率。但非授权频段缺乏相互干扰协调及管理机制,通过分析目前3种LTE-U的干扰检测及避免方法的...为了满足用户对高速率、低时延的网络需求,解决现网频谱资源稀缺的问题,介绍了一种非授权频段与授权频段载波聚合的方法,来提高频谱资源的利用率。但非授权频段缺乏相互干扰协调及管理机制,通过分析目前3种LTE-U的干扰检测及避免方法的优缺点,研究了一种LTE-U干扰检测及避免的算法,该算法能实现LTE-U Small Cell干扰的自动化检测和优化,从而达到提升LTE-U Small Cell工作可靠性的效果。展开更多
The rapid growth of mobile data traffic has caused great pressure on the limited spectrum resources,and there must be some better methods to deal with this problem.The innovative technology of Long-Term Evolution(LTE)u...The rapid growth of mobile data traffic has caused great pressure on the limited spectrum resources,and there must be some better methods to deal with this problem.The innovative technology of Long-Term Evolution(LTE)using the unlicensed spectrum,known as LTE-Unlicensed(LTE-U),has been proposed to effectively alleviate the shortage of authorized band resources.LTE-U has explored a lot of potential capacity in mobile communication systems with limited authorized spectrum resources,and improved the spectrum utilization of unauthorized frequency bands.However,LTE-U is still facing challenges in its application.In this paper,we summarize the key features of LTE-U and the coex-istence of LTE-U with Wi-Fi in the unlicensed Spectrum.We analyze the key technologies(including carrier aggregation,HARQ,interference cancelation,and centralized scheduling),the operating modes and deployment scenarios(including carrier aggregation LTE-U,duty cycle LTE-U,and standalone LTE-U),and the advantages(including anchored LTE-U and Standalone LTE-U scenarios),as well as main technical challenges.We then address the different management mechanisms of LTE-U and Wi-Fi(including the differences between the MAC layer and physical layer),the types of coexistence technology classification(including channel separation and channel sharing technologies),and directions for future work.We hope that this comprehensive survey spurs further research in this promising area.展开更多
文摘LTE-U(LTE in unlicensed spectrum)是近来3GPP标准化组织讨论研究的用于缓解授权频段通信压力的新兴技术。首先对LTE-U技术进行概述,随后分析了LTE-U的工作频谱和设计要求,同时总结了其可能的部署场景和运行模式,然后对部署LTE-U存在的难点和现有的应用解决方案的相关研究进行了详细阐述,最后对未来的研究方向进行了展望。
文摘无线数据业务的快速增长给有限的频谱资源带来新的挑战。在当前的各种提高频谱效率方案中,LTE-U(LTE in unlicensed spectrum,LTE-U)通信系统获得了全球运营商的广泛认可。作为5G关键技术之一,LTE-U运用载波聚合技术(carrier aggregation,CA)借助于非授权频段对数据业务进行分流,以达到提高网络数据传输速率、频谱利用率和增强用户移动性的目的。然而,由于LTE-U和WiFi系统接入技术的不同,如何解决两个系统之间的和谐共存成为LTE-U系统能否在非授权频段上使用的关键。对LTE-U的相关背景知识、工作模式、载波聚合技术、LTE-U设计要点进行介绍。指出当前LTE-U和WiFi在共存问题上面临的挑战,并对当前LTE-U和WiFi系统在非授权频段上共存的解决方案进行阐述分析和对比。对未来LTE-U和WiFi系统共存的研究方向进行了展望。
基金supported in part by the NSF China under Grant(61801365,61701365,61971327,61901319)in part by the China Postdoctoral Science Foundation under Grant(2018M643581,2018M633464,2019TQ0210,2019M663015)+5 种基金in part by Natural Science Foundation of Shaanxi Province(2019JQ-152,2020JQ-686)in part by Young Talent fund of University Association for Science and Technology in Shaanxi,China(20200112)in part by Natural Science Basic Research Plan in Shaanxi Province of China(2020JQ-328)in part by Natural Science Foundation of the Jiangsu Higher Education Institutions(19KJB510021)in part by Postdoctoral Foundation in Shaanxi Province of Chinathe Fundamental Research Funds for the Central Universities.
文摘This paper proposes a deep learning(DL)resource allocation framework to achieve the harmonious coexistence between the transceiver pairs(TPs)and the Wi-Fi users in LTE-U networks.The nonconvex resource allocation is considered as a constrained learning problem and the deep neural network(DNN)is employed to approximate the optimal resource allocation decisions through unsupervised manner.A parallel DNN framework is proposed to deal with the two optimization variables in this problem,where one is the licensed power allocation unit and the other is the unlicensed time fraction occupied unit.Besides,to guarantee the feasibility of the proposed algorithm,the Lagrange dual method is used to relax the constraints into the DNN training process.Then,the dual variable and the DNN parameter are alternating update via the batch-based gradient decent method until the training process converges.Numerical results show that the proposed algorithm is feasible and has better performance than other general algorithms.
文摘为了满足用户对高速率、低时延的网络需求,解决现网频谱资源稀缺的问题,介绍了一种非授权频段与授权频段载波聚合的方法,来提高频谱资源的利用率。但非授权频段缺乏相互干扰协调及管理机制,通过分析目前3种LTE-U的干扰检测及避免方法的优缺点,研究了一种LTE-U干扰检测及避免的算法,该算法能实现LTE-U Small Cell干扰的自动化检测和优化,从而达到提升LTE-U Small Cell工作可靠性的效果。
文摘The rapid growth of mobile data traffic has caused great pressure on the limited spectrum resources,and there must be some better methods to deal with this problem.The innovative technology of Long-Term Evolution(LTE)using the unlicensed spectrum,known as LTE-Unlicensed(LTE-U),has been proposed to effectively alleviate the shortage of authorized band resources.LTE-U has explored a lot of potential capacity in mobile communication systems with limited authorized spectrum resources,and improved the spectrum utilization of unauthorized frequency bands.However,LTE-U is still facing challenges in its application.In this paper,we summarize the key features of LTE-U and the coex-istence of LTE-U with Wi-Fi in the unlicensed Spectrum.We analyze the key technologies(including carrier aggregation,HARQ,interference cancelation,and centralized scheduling),the operating modes and deployment scenarios(including carrier aggregation LTE-U,duty cycle LTE-U,and standalone LTE-U),and the advantages(including anchored LTE-U and Standalone LTE-U scenarios),as well as main technical challenges.We then address the different management mechanisms of LTE-U and Wi-Fi(including the differences between the MAC layer and physical layer),the types of coexistence technology classification(including channel separation and channel sharing technologies),and directions for future work.We hope that this comprehensive survey spurs further research in this promising area.
