Pub Date : 2020-11-03DOI: 10.12142/ZTECOM.202003002
He Yejun, Jiang Jiachun, Zhang Long, Li Wenting, Wong Sai-Wai, Deng Wei, Chi Baoyong
Since leaky-wave antennas (LWAs) have the advantages of high directivity, low loss and structural simplicity, LWAs are very suitable for designing millimeter-wave (mmW) antennas. The purpose of this paper is to review the latest research progress of LWAs for 5G/ B5G mobile communication systems. Firstly, the conventional classification and design methods of LWAs are introduced and the effects of the phase constant and attenuation con‐ stant on the radiation characteristics are discussed. Then two types of new LWAs for 5G/ B5G mobile communication systems including broadband fixed-beam LWAs and frequencyfixed beam-scanning LWAs are summarized. Finally, the challenges and future research di‐ rections of LWAs for 5G/B5G mobile communication systems are presented.
{"title":"Leaky-Wave Antennas for 5G/B5G Mobile Communication Systems: A Survey","authors":"He Yejun, Jiang Jiachun, Zhang Long, Li Wenting, Wong Sai-Wai, Deng Wei, Chi Baoyong","doi":"10.12142/ZTECOM.202003002","DOIUrl":"https://doi.org/10.12142/ZTECOM.202003002","url":null,"abstract":"Since leaky-wave antennas (LWAs) have the advantages of high directivity, low loss and structural simplicity, LWAs are very suitable for designing millimeter-wave (mmW) antennas. The purpose of this paper is to review the latest research progress of LWAs for 5G/ B5G mobile communication systems. Firstly, the conventional classification and design methods of LWAs are introduced and the effects of the phase constant and attenuation con‐ stant on the radiation characteristics are discussed. Then two types of new LWAs for 5G/ B5G mobile communication systems including broadband fixed-beam LWAs and frequencyfixed beam-scanning LWAs are summarized. Finally, the challenges and future research di‐ rections of LWAs for 5G/B5G mobile communication systems are presented.","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":"18 1","pages":"3-11"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44307075","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}
Pub Date : 2020-08-07DOI: 10.12142/ZTECOM.202002001
Tao Meixia, Hu Kaibin
{"title":"Editorial: Special Topic onMachine Learning at Network Edges","authors":"Tao Meixia, Hu Kaibin","doi":"10.12142/ZTECOM.202002001","DOIUrl":"https://doi.org/10.12142/ZTECOM.202002001","url":null,"abstract":"","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":"18 1","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48381539","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}
Pub Date : 2020-08-07DOI: 10.12142/ZTECOM.202002002
H. Howard, Zhao Zhongyuan, Tony Q. S. Quek
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.
{"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":"18 1","pages":"2-10"},"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}
Pub Date : 2020-08-07DOI: 10.12142/ZTECOM.202002004
Jiang Zhihui, He Ying-hui, Yu Guanding
{"title":"Joint User Selection and Resource Allocation for Fast Federated Edge Learning","authors":"Jiang Zhihui, He Ying-hui, Yu Guanding","doi":"10.12142/ZTECOM.202002004","DOIUrl":"https://doi.org/10.12142/ZTECOM.202002004","url":null,"abstract":"","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":"18 1","pages":"20-30"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43656326","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}
Pub Date : 2020-08-07DOI: 10.12142/ZTECOM.202002008
Sun-ting Lin, Du Jiangbing, H. Feng, Tang Ningfeng, H. Zuyuan
{"title":"Adaptive and Intelligent Digital Signal Processing for Improved Optical Interconnection","authors":"Sun-ting Lin, Du Jiangbing, H. Feng, Tang Ningfeng, H. Zuyuan","doi":"10.12142/ZTECOM.202002008","DOIUrl":"https://doi.org/10.12142/ZTECOM.202002008","url":null,"abstract":"","PeriodicalId":61991,"journal":{"name":"ZTE Communications","volume":"18 1","pages":"57-73"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48699163","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}
Pub Date : 2020-08-07DOI: 10.12142/ZTECOM.202002003
Shi Wenqi, Sun Yuxuan, Huang Xiufeng, Zhou Sheng, Niu Zhisheng
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.
{"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":"18 1","pages":"11-19"},"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}
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