{"title":"面向行人位置感知 5G 多播/广播服务的联合多任务学习","authors":"Zexuan Jing;Junsheng Mu;Jian Jin;Zhenzhen Jiao;Peng Yu","doi":"10.1109/TBC.2023.3332012","DOIUrl":null,"url":null,"abstract":"5G multicast/broadcast services can provide transformative new opportunities as mobile devices proliferate. However, realizing the full potential of these services requires real-time pedestrian localization. We propose a federated multitask learning (FML) approach on smartphones to enable pedestrian location-aware 5G multicast/broadcast services. Our lightweight FML architecture provides accurate real-time localization while preserving privacy. The pedestrian location data enables adaptive 5G network planning, contextual location-based services, quality of service improvements, and load balancing. Simulations demonstrate the effectiveness of our FML scheme for accurate pedestrian localization. They also highlight significant enhancements to 5G multicast/broadcast services enabled by real-time pedestrian positioning. In summary, our work facilitates enhanced 5G multicast/broadcast services through federated on-device learning for real-time pedestrian localization.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 1","pages":"66-77"},"PeriodicalIF":3.2000,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Federated Multitask Learning for Pedestrian Location-Aware 5G Multicast/Broadcast Services\",\"authors\":\"Zexuan Jing;Junsheng Mu;Jian Jin;Zhenzhen Jiao;Peng Yu\",\"doi\":\"10.1109/TBC.2023.3332012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"5G multicast/broadcast services can provide transformative new opportunities as mobile devices proliferate. However, realizing the full potential of these services requires real-time pedestrian localization. We propose a federated multitask learning (FML) approach on smartphones to enable pedestrian location-aware 5G multicast/broadcast services. Our lightweight FML architecture provides accurate real-time localization while preserving privacy. The pedestrian location data enables adaptive 5G network planning, contextual location-based services, quality of service improvements, and load balancing. Simulations demonstrate the effectiveness of our FML scheme for accurate pedestrian localization. They also highlight significant enhancements to 5G multicast/broadcast services enabled by real-time pedestrian positioning. In summary, our work facilitates enhanced 5G multicast/broadcast services through federated on-device learning for real-time pedestrian localization.\",\"PeriodicalId\":13159,\"journal\":{\"name\":\"IEEE Transactions on Broadcasting\",\"volume\":\"70 1\",\"pages\":\"66-77\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2023-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Broadcasting\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10361599/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Broadcasting","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10361599/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Federated Multitask Learning for Pedestrian Location-Aware 5G Multicast/Broadcast Services
5G multicast/broadcast services can provide transformative new opportunities as mobile devices proliferate. However, realizing the full potential of these services requires real-time pedestrian localization. We propose a federated multitask learning (FML) approach on smartphones to enable pedestrian location-aware 5G multicast/broadcast services. Our lightweight FML architecture provides accurate real-time localization while preserving privacy. The pedestrian location data enables adaptive 5G network planning, contextual location-based services, quality of service improvements, and load balancing. Simulations demonstrate the effectiveness of our FML scheme for accurate pedestrian localization. They also highlight significant enhancements to 5G multicast/broadcast services enabled by real-time pedestrian positioning. In summary, our work facilitates enhanced 5G multicast/broadcast services through federated on-device learning for real-time pedestrian localization.
期刊介绍：
The Society’s Field of Interest is “Devices, equipment, techniques and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.” In addition to this formal FOI statement, which is used to provide guidance to the Publications Committee in the selection of content, the AdCom has further resolved that “broadcast systems includes all aspects of transmission, propagation, and reception.”
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