{"title":"带卷积层的 PRNet 用于降低 OFDM 信号的 PAPR","authors":"Masaya Ohta;Koichi Kubota","doi":"10.23919/comex.2024XBL0091","DOIUrl":null,"url":null,"abstract":"This research uses deep learning to address the high peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM), which is critical for wireless communications. Although a PAPR-reducing network (PRNet), which is a deep learning model, can be used to suppress the PAPR, its computational cost is huge. In this research, the number of layers in a PRNet model is optimized and a fully connected layer is replaced with a convolution layer to reduce the computational load.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"13 8","pages":"339-342"},"PeriodicalIF":0.3000,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10554794","citationCount":"0","resultStr":"{\"title\":\"PRNet with Convolution Layer for PAPR Reduction of OFDM Signals\",\"authors\":\"Masaya Ohta;Koichi Kubota\",\"doi\":\"10.23919/comex.2024XBL0091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research uses deep learning to address the high peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM), which is critical for wireless communications. Although a PAPR-reducing network (PRNet), which is a deep learning model, can be used to suppress the PAPR, its computational cost is huge. In this research, the number of layers in a PRNet model is optimized and a fully connected layer is replaced with a convolution layer to reduce the computational load.\",\"PeriodicalId\":54101,\"journal\":{\"name\":\"IEICE Communications Express\",\"volume\":\"13 8\",\"pages\":\"339-342\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2024-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10554794\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEICE Communications Express\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10554794/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEICE Communications Express","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10554794/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
PRNet with Convolution Layer for PAPR Reduction of OFDM Signals
This research uses deep learning to address the high peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM), which is critical for wireless communications. Although a PAPR-reducing network (PRNet), which is a deep learning model, can be used to suppress the PAPR, its computational cost is huge. In this research, the number of layers in a PRNet model is optimized and a fully connected layer is replaced with a convolution layer to reduce the computational load.
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