{"title":"基于集中式人工智能的频谱决策多通道软件无线电","authors":"Vlad Fernoaga, R. Curpen, Cosmin Nutiu, F. Sandu","doi":"10.1109/ROEDUNET.2019.8909454","DOIUrl":null,"url":null,"abstract":"The present paper aims to bring Artificial Intelligence (AI) in Software Defined Radio (SDR). A multichannel spectrum sensing problem, extended to a long-term spectral occupancy observation, enabled the authors to derive a “vertical” per-channel machine learning model that was tested in an integrated National Instruments (NI) environment – Ettus/NI USRP (Universal Software Radio Peripherals) service-driven, top-down, by LabVIEW. The proof-of-concept was based on a simple 8 channels PMR (Private Mobile Radio) use-case.","PeriodicalId":309683,"journal":{"name":"2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet)","volume":"48 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multichannel Software Defined Radio with Spectral Decision via Centralized Artificial Intelligence\",\"authors\":\"Vlad Fernoaga, R. Curpen, Cosmin Nutiu, F. Sandu\",\"doi\":\"10.1109/ROEDUNET.2019.8909454\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The present paper aims to bring Artificial Intelligence (AI) in Software Defined Radio (SDR). A multichannel spectrum sensing problem, extended to a long-term spectral occupancy observation, enabled the authors to derive a “vertical” per-channel machine learning model that was tested in an integrated National Instruments (NI) environment – Ettus/NI USRP (Universal Software Radio Peripherals) service-driven, top-down, by LabVIEW. The proof-of-concept was based on a simple 8 channels PMR (Private Mobile Radio) use-case.\",\"PeriodicalId\":309683,\"journal\":{\"name\":\"2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet)\",\"volume\":\"48 5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROEDUNET.2019.8909454\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROEDUNET.2019.8909454","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multichannel Software Defined Radio with Spectral Decision via Centralized Artificial Intelligence
The present paper aims to bring Artificial Intelligence (AI) in Software Defined Radio (SDR). A multichannel spectrum sensing problem, extended to a long-term spectral occupancy observation, enabled the authors to derive a “vertical” per-channel machine learning model that was tested in an integrated National Instruments (NI) environment – Ettus/NI USRP (Universal Software Radio Peripherals) service-driven, top-down, by LabVIEW. The proof-of-concept was based on a simple 8 channels PMR (Private Mobile Radio) use-case.
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