{"title":"训练模型对适用于5G系统的深度联合源信道编码的影响","authors":"Ryunosuke Yamamoto;Keigo Matsumoto;Yoshiaki Inoue;Yuko Hara;Kazuki Maruta;Yu Nakayama;Daisuke Hisano","doi":"10.23919/comex.2024COL0014","DOIUrl":null,"url":null,"abstract":"With the development of 5G technology and the proliferation of IoT devices, Deep Joint Source-Channel Coding (DeepJSCC) has attracted attention for efficiently transmitting video and image data. DeepJSCC can maintain a good peak signal-to-noise ratio (PSNR) of images even at a meager signal-to-noise ratio (SNR). In cellular communication systems, the compression ratio must adapt to channel fluctuations, requiring multiple training models at the base station. However, the optimal SNR and compression ratio combination during training has yet to be reported. This paper investigates the necessary number of training models by stepwise varying SNR and compression ratio during training.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"13 12","pages":"466-469"},"PeriodicalIF":0.3000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10633225","citationCount":"0","resultStr":"{\"title\":\"Impact of Training Models on Deep Joint Source-Channel Coding Applicable to 5G Systems\",\"authors\":\"Ryunosuke Yamamoto;Keigo Matsumoto;Yoshiaki Inoue;Yuko Hara;Kazuki Maruta;Yu Nakayama;Daisuke Hisano\",\"doi\":\"10.23919/comex.2024COL0014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the development of 5G technology and the proliferation of IoT devices, Deep Joint Source-Channel Coding (DeepJSCC) has attracted attention for efficiently transmitting video and image data. DeepJSCC can maintain a good peak signal-to-noise ratio (PSNR) of images even at a meager signal-to-noise ratio (SNR). In cellular communication systems, the compression ratio must adapt to channel fluctuations, requiring multiple training models at the base station. However, the optimal SNR and compression ratio combination during training has yet to be reported. This paper investigates the necessary number of training models by stepwise varying SNR and compression ratio during training.\",\"PeriodicalId\":54101,\"journal\":{\"name\":\"IEICE Communications Express\",\"volume\":\"13 12\",\"pages\":\"466-469\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2024-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10633225\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEICE Communications Express\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10633225/\",\"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/10633225/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Impact of Training Models on Deep Joint Source-Channel Coding Applicable to 5G Systems
With the development of 5G technology and the proliferation of IoT devices, Deep Joint Source-Channel Coding (DeepJSCC) has attracted attention for efficiently transmitting video and image data. DeepJSCC can maintain a good peak signal-to-noise ratio (PSNR) of images even at a meager signal-to-noise ratio (SNR). In cellular communication systems, the compression ratio must adapt to channel fluctuations, requiring multiple training models at the base station. However, the optimal SNR and compression ratio combination during training has yet to be reported. This paper investigates the necessary number of training models by stepwise varying SNR and compression ratio during training.
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