Serhat Tadik;Kaitlyn M. Graves;Michael A. Varner;Christopher R. Anderson;David M. Johnson;Sneha Kumar Kasera;Neal Patwari;Jacobus Van der Merwe;Gregory D. Durgin
{"title":"Digital Spectrum Twins for Enhanced Spectrum Sharing and Other Radio Applications","authors":"Serhat Tadik;Kaitlyn M. Graves;Michael A. Varner;Christopher R. Anderson;David M. Johnson;Sneha Kumar Kasera;Neal Patwari;Jacobus Van der Merwe;Gregory D. Durgin","doi":"10.1109/JRFID.2023.3327212","DOIUrl":null,"url":null,"abstract":"This paper outlines the components of a digital spectrum twin (DST) and potential application maps that can inform automated or enhanced spectrum management decisions. The DST is fundamentally a map and image database, with environmental, measurement, and prediction maps that allow parallel intelligence operations to generate useful information using aggregating rules that operate on the twin. We demonstrate several application maps generated from measured data collections and propagation modeling associated with the POWDER platform in Salt Lake City, Utah. In total, the methods of this paper provide a blueprint for generating similar DSTs in any other radio bands and regions of the world.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"376-391"},"PeriodicalIF":2.3000,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10293151","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE journal of radio frequency identification","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10293151/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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Abstract
This paper outlines the components of a digital spectrum twin (DST) and potential application maps that can inform automated or enhanced spectrum management decisions. The DST is fundamentally a map and image database, with environmental, measurement, and prediction maps that allow parallel intelligence operations to generate useful information using aggregating rules that operate on the twin. We demonstrate several application maps generated from measured data collections and propagation modeling associated with the POWDER platform in Salt Lake City, Utah. In total, the methods of this paper provide a blueprint for generating similar DSTs in any other radio bands and regions of the world.
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