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(ICLR)"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2019.2952051"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2961673"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2974748"},{"key":"ref116","article-title":"Over-the-air computing for wireless data aggregation in massive IoT","author":"zhu","year":"2020","journal-title":"arXiv 2009 02181"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3036948"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2917084"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.2999852"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2975559"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2017.8007057"}],"container-title":["Proceedings of the 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