{"title":"基于RRDBNet的OFDM信道估计","authors":"Wei Gao, Meihong Yang, Wei Zhang, Libin Liu","doi":"10.1109/ISCC55528.2022.9912769","DOIUrl":null,"url":null,"abstract":"Channel estimation is important for orthogonal frequency division multiplexing (OFDM) in current wireless communication systems. Prevalent channel estimation algorithms, however, cannot be widely deployed due to some practical reasons, such as poor robustness and high computational complexity. To solve the problems for OFDM systems, we propose a new channel estimation scheme with a fine-designed deep learning model, called RRDBNet. RRDBNet can be trained easily while maintaining the advantages of residual learning and increasing the structure capacity, by combining the multi-level residual network and dense links. Our simulation results show that RRDBNet outperforms the traditional least-square algorithm and existing DL-based super-resolution schemes, which ranges from 0.5 to 1dB at low SNR and from 2 to 3dB at high SNR. Besides, in terms of the number of pilots, RRDBNet is also superior to existing schemes and approaches LMMSE.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Efficient OFDM Channel Estimation with RRDBNet\",\"authors\":\"Wei Gao, Meihong Yang, Wei Zhang, Libin Liu\",\"doi\":\"10.1109/ISCC55528.2022.9912769\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Channel estimation is important for orthogonal frequency division multiplexing (OFDM) in current wireless communication systems. Prevalent channel estimation algorithms, however, cannot be widely deployed due to some practical reasons, such as poor robustness and high computational complexity. To solve the problems for OFDM systems, we propose a new channel estimation scheme with a fine-designed deep learning model, called RRDBNet. RRDBNet can be trained easily while maintaining the advantages of residual learning and increasing the structure capacity, by combining the multi-level residual network and dense links. Our simulation results show that RRDBNet outperforms the traditional least-square algorithm and existing DL-based super-resolution schemes, which ranges from 0.5 to 1dB at low SNR and from 2 to 3dB at high SNR. Besides, in terms of the number of pilots, RRDBNet is also superior to existing schemes and approaches LMMSE.\",\"PeriodicalId\":309606,\"journal\":{\"name\":\"2022 IEEE Symposium on Computers and Communications (ISCC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Symposium on Computers and Communications (ISCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCC55528.2022.9912769\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Symposium on Computers and Communications (ISCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCC55528.2022.9912769","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Channel estimation is important for orthogonal frequency division multiplexing (OFDM) in current wireless communication systems. Prevalent channel estimation algorithms, however, cannot be widely deployed due to some practical reasons, such as poor robustness and high computational complexity. To solve the problems for OFDM systems, we propose a new channel estimation scheme with a fine-designed deep learning model, called RRDBNet. RRDBNet can be trained easily while maintaining the advantages of residual learning and increasing the structure capacity, by combining the multi-level residual network and dense links. Our simulation results show that RRDBNet outperforms the traditional least-square algorithm and existing DL-based super-resolution schemes, which ranges from 0.5 to 1dB at low SNR and from 2 to 3dB at high SNR. Besides, in terms of the number of pilots, RRDBNet is also superior to existing schemes and approaches LMMSE.