ZTE Communications最新文献_第4页

Leaky-Wave Antennas for 5G/B5G Mobile Communication Systems: A Survey 5G/B5G移动通信系统漏波天线研究进展

ZTE Communications Pub Date : 2020-11-03 DOI: 10.12142/ZTECOM.202003002

He Yejun, Jiang Jiachun, Zhang Long, Li Wenting, Wong Sai-Wai, Deng Wei, Chi Baoyong

引用次数: 1

Design of Millimeter-Wave Antenna-in-Package (AiP) for 5G NR 5G新空口毫米波封装天线(AiP)设计

ZTE Communications Pub Date : 2020-11-03 DOI: 10.12142/ZTECOM.202003005

Chang Su-Wei, Lin Chueh-Jen, Tsai Wen-Tsai, Hung Tzu-Chieh, Huang Po-Chia

引用次数: 2

A Survey of Wi-Fi Sensing Techniques with Channel State Information 基于信道状态信息的Wi-Fi传感技术综述

ZTE Communications Pub Date : 2020-11-03 DOI: 10.12142/ZTECOM.202003009

Chen Liangqin, Tian Liping, Xu Zhimeng, Chen Zhizhang

引用次数: 0

Crowd Counting for Real Monitoring Scene 实时监控场景的人群计数

ZTE Communications Pub Date : 2020-08-07 DOI: 10.12142/ZTECOM.202002009

L. Yiming, Liao Weihua, Shen Zan, Ni Bingbing

引用次数: 1

Editorial: Special Topic onMachine Learning at Network Edges 社论：网络边缘的机器学习专题

ZTE Communications Pub Date : 2020-08-07 DOI: 10.12142/ZTECOM.202002001

Tao Meixia, Hu Kaibin

引用次数: 3

Joint Placement and Resource Allocation for UAV-Assisted Mobile Edge Computing Networks with URLLC 无人机辅助移动边缘计算网络与URLLC的联合布局和资源分配

ZTE Communications Pub Date : 2020-08-07 DOI: 10.12142/ZTECOM.202002007

Zhang Pengyu, X. Lifeng, Xu Jie

引用次数: 1

Enabling Intelligence at Network Edge:An Overview of Federated Learning 在网络边缘实现智能：联合学习综述

ZTE Communications Pub Date : 2020-08-07 DOI: 10.12142/ZTECOM.202002002

H. Howard, Zhao Zhongyuan, Tony Q. S. Quek

{"title":"Enabling Intelligence at Network Edge:An Overview of Federated Learning","authors":"H. Howard, Zhao Zhongyuan, Tony Q. S. Quek","doi":"10.12142/ZTECOM.202002002","DOIUrl":"https://doi.org/10.12142/ZTECOM.202002002","url":null,"abstract":"The burgeoning advances in machine learning and wireless technologies are forg⁃ ing a new paradigm for future networks, which are expected to possess higher degrees of in⁃ telligence via the inference from vast dataset and being able to respond to local events in a timely manner. Due to the sheer volume of data generated by end-user devices, as well as the increasing concerns about sharing private information, a new branch of machine learn⁃ ing models, namely federated learning, has emerged from the intersection of artificial intelli⁃ gence and edge computing. In contrast to conventional machine learning methods, federated learning brings the models directly to the device for training, where only the resultant param⁃ eters shall be sent to the edge servers. The local copies of the model on the devices bring along great advantages of eliminating network latency and preserving data privacy. Never⁃ theless, to make federated learning possible, one needs to tackle new challenges that require a fundamental departure from standard methods designed for distributed optimizations. In this paper, we aim to deliver a comprehensive introduction of federated learning. Specifical⁃ ly, we first survey the basis of federated learning, including its learning structure and the distinct features from conventional machine learning models. We then enumerate several critical issues associated with the deployment of federated learning in a wireless network, and show why and how technologies should be jointly integrated to facilitate the full imple⁃ mentation from different perspectives, ranging from algorithmic design, on-device training, to communication resource management. Finally, we conclude by shedding light on some po⁃ tential applications and future trends.","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45519559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Joint User Selection and Resource Allocation for Fast Federated Edge Learning 快速联邦边缘学习的联合用户选择和资源分配

ZTE Communications Pub Date : 2020-08-07 DOI: 10.12142/ZTECOM.202002004

Jiang Zhihui, He Ying-hui, Yu Guanding

引用次数: 0

Adaptive and Intelligent Digital Signal Processing for Improved Optical Interconnection 用于改进光互连的自适应智能数字信号处理

ZTE Communications Pub Date : 2020-08-07 DOI: 10.12142/ZTECOM.202002008

Sun-ting Lin, Du Jiangbing, H. Feng, Tang Ningfeng, H. Zuyuan

引用次数: 2

Scheduling Policies for Federated Learning in Wireless Networks: An Overview 无线网络中联邦学习的调度策略综述

ZTE Communications Pub Date : 2020-08-07 DOI: 10.12142/ZTECOM.202002003

Shi Wenqi, Sun Yuxuan, Huang Xiufeng, Zhou Sheng, Niu Zhisheng

{"title":"Scheduling Policies for Federated Learning in Wireless Networks: An Overview","authors":"Shi Wenqi, Sun Yuxuan, Huang Xiufeng, Zhou Sheng, Niu Zhisheng","doi":"10.12142/ZTECOM.202002003","DOIUrl":"https://doi.org/10.12142/ZTECOM.202002003","url":null,"abstract":"Due to the increasing need for massive data analysis and machine learning model training at the network edge, as well as the rising concerns about data privacy, a new distrib⁃ uted training framework called federated learning (FL) has emerged and attracted much at⁃ tention from both academia and industry. In FL, participating devices iteratively update the local models based on their own data and contribute to the global training by uploading mod⁃ el updates until the training converges. Therefore, the computation capabilities of mobile de⁃ vices can be utilized and the data privacy can be preserved. However, deploying FL in re⁃ source-constrained wireless networks encounters several challenges, including the limited energy of mobile devices, weak onboard computing capability, and scarce wireless band⁃ width. To address these challenges, recent solutions have been proposed to maximize the convergence rate or minimize the energy consumption under heterogeneous constraints. In this overview, we first introduce the backgrounds and fundamentals of FL. Then, the key challenges in deploying FL in wireless networks are discussed, and several existing solu⁃ tions are reviewed. Finally, we highlight the open issues and future research directions in FL scheduling.","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43316559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4