{"title":"基于YOLOv5的WBSig53数据集调制识别","authors":"B. Comar","doi":"10.1109/WOCC58016.2023.10139594","DOIUrl":null,"url":null,"abstract":"This paper discusses applying a set of YOLOv5 neural networks to the WBSig53 dataset in order to perform modulation recognition. Identifying modulation schemes is a main step in the development of smart receivers. In this effort, attention is payed to the amount of time needed for training as well as inference speed. Signal detection, modulation family classification, and individual modulation scheme recognition are explored on clean and impaired WBSig53 data.","PeriodicalId":226792,"journal":{"name":"2023 32nd Wireless and Optical Communications Conference (WOCC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modulation Recognition using YOLOv5 on the WBSig53 Dataset\",\"authors\":\"B. Comar\",\"doi\":\"10.1109/WOCC58016.2023.10139594\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper discusses applying a set of YOLOv5 neural networks to the WBSig53 dataset in order to perform modulation recognition. Identifying modulation schemes is a main step in the development of smart receivers. In this effort, attention is payed to the amount of time needed for training as well as inference speed. Signal detection, modulation family classification, and individual modulation scheme recognition are explored on clean and impaired WBSig53 data.\",\"PeriodicalId\":226792,\"journal\":{\"name\":\"2023 32nd Wireless and Optical Communications Conference (WOCC)\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 32nd Wireless and Optical Communications Conference (WOCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WOCC58016.2023.10139594\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 32nd Wireless and Optical Communications Conference (WOCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WOCC58016.2023.10139594","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modulation Recognition using YOLOv5 on the WBSig53 Dataset
This paper discusses applying a set of YOLOv5 neural networks to the WBSig53 dataset in order to perform modulation recognition. Identifying modulation schemes is a main step in the development of smart receivers. In this effort, attention is payed to the amount of time needed for training as well as inference speed. Signal detection, modulation family classification, and individual modulation scheme recognition are explored on clean and impaired WBSig53 data.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
