{"title":"一种移动OFDM系统中DFO与信道估计相结合的方案","authors":"Lihua Yang, Yongqi Shao, Ao Chang, Bo Hu","doi":"10.1049/cmu2.12851","DOIUrl":null,"url":null,"abstract":"<p>To reduce the impact of residual Doppler frequency offset (RDFO), a joint DFO estimation and CE scheme is proposed for the OFDM systems under the high-speed mobile environment. In the paper, the expression of interference power caused by the RDFO is first derived, and the effect of RDFO on time-varying characteristic of channel is analysed. Then, a joint DFO and channel estimation scheme is presented. Specifically, a high-precision DFO estimator based on the convolutional neural network with anti-noise is firstly designed. Due to its ability to use fewer samples to adapt well to the new environments, the meta learning is adopted to estimate the time-varying channel. Moreover, to improve the practicality of the algorithm, the non-ideal values rather than ideal values are used as the training targets in the two neural networks. Additionally, the proposed method is only based on the received signal and does not require any pilots or training sequences, which has higher transmission efficiency compared to the existing algorithms. The research results indicate that the proposed method has good estimation performance and good practicality, and it is suitable for high-speed mobile scenarios.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"18 19","pages":"1564-1573"},"PeriodicalIF":1.5000,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.12851","citationCount":"0","resultStr":"{\"title\":\"A scheme of combining DFO and channel estimation scheme for mobile OFDM systems\",\"authors\":\"Lihua Yang, Yongqi Shao, Ao Chang, Bo Hu\",\"doi\":\"10.1049/cmu2.12851\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>To reduce the impact of residual Doppler frequency offset (RDFO), a joint DFO estimation and CE scheme is proposed for the OFDM systems under the high-speed mobile environment. In the paper, the expression of interference power caused by the RDFO is first derived, and the effect of RDFO on time-varying characteristic of channel is analysed. Then, a joint DFO and channel estimation scheme is presented. Specifically, a high-precision DFO estimator based on the convolutional neural network with anti-noise is firstly designed. Due to its ability to use fewer samples to adapt well to the new environments, the meta learning is adopted to estimate the time-varying channel. Moreover, to improve the practicality of the algorithm, the non-ideal values rather than ideal values are used as the training targets in the two neural networks. Additionally, the proposed method is only based on the received signal and does not require any pilots or training sequences, which has higher transmission efficiency compared to the existing algorithms. The research results indicate that the proposed method has good estimation performance and good practicality, and it is suitable for high-speed mobile scenarios.</p>\",\"PeriodicalId\":55001,\"journal\":{\"name\":\"IET Communications\",\"volume\":\"18 19\",\"pages\":\"1564-1573\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.12851\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Communications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/cmu2.12851\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Communications","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cmu2.12851","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A scheme of combining DFO and channel estimation scheme for mobile OFDM systems
To reduce the impact of residual Doppler frequency offset (RDFO), a joint DFO estimation and CE scheme is proposed for the OFDM systems under the high-speed mobile environment. In the paper, the expression of interference power caused by the RDFO is first derived, and the effect of RDFO on time-varying characteristic of channel is analysed. Then, a joint DFO and channel estimation scheme is presented. Specifically, a high-precision DFO estimator based on the convolutional neural network with anti-noise is firstly designed. Due to its ability to use fewer samples to adapt well to the new environments, the meta learning is adopted to estimate the time-varying channel. Moreover, to improve the practicality of the algorithm, the non-ideal values rather than ideal values are used as the training targets in the two neural networks. Additionally, the proposed method is only based on the received signal and does not require any pilots or training sequences, which has higher transmission efficiency compared to the existing algorithms. The research results indicate that the proposed method has good estimation performance and good practicality, and it is suitable for high-speed mobile scenarios.
期刊介绍:
IET Communications covers the fundamental and generic research for a better understanding of communication technologies to harness the signals for better performing communication systems using various wired and/or wireless media. This Journal is particularly interested in research papers reporting novel solutions to the dominating problems of noise, interference, timing and errors for reduction systems deficiencies such as wasting scarce resources such as spectra, energy and bandwidth.
Topics include, but are not limited to:
Coding and Communication Theory;
Modulation and Signal Design;
Wired, Wireless and Optical Communication;
Communication System
Special Issues. Current Call for Papers:
Cognitive and AI-enabled Wireless and Mobile - https://digital-library.theiet.org/files/IET_COM_CFP_CAWM.pdf
UAV-Enabled Mobile Edge Computing - https://digital-library.theiet.org/files/IET_COM_CFP_UAV.pdf