STO Estimation for OFDM System Using CDM

In this letter, we propose a novel scheme for symbol timing offset (STO) estimation by using a convolutional neural network (CNN)-deep neural network (DNN) model (CDM) architecture for the orthogonal frequency division multiplexing (OFDM) system over different fading channel models. The proposed sch...

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Bibliographic Details
Published in:IEEE communications letters 2022-11, Vol.26 (11), p.2651-2655
Main Authors: Chaudhari, Mahesh Shamrao, Majhi, Sudhan
Format: Article
Language:English
Subjects:
Artificial neural networks
Carrier frequencies
CFO
CNN-DNN
Convolution
CSI
Estimation
OFDM
Orthogonal Frequency Division Multiplexing
Rician channels
Signal to noise ratio
STO
Symbols
Training
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Summary:In this letter, we propose a novel scheme for symbol timing offset (STO) estimation by using a convolutional neural network (CNN)-deep neural network (DNN) model (CDM) architecture for the orthogonal frequency division multiplexing (OFDM) system over different fading channel models. The proposed scheme estimates STO in the presence of carrier frequency offset (CFO) and without prior knowledge of the channel, modulation format, and transmitted OFDM signal parameters. The CDM architecture achieves better estimation accuracy gain compared to that of statistical-based and pilot-assisted CNN-based methods. Finally, the proposed CDM is validated over a radio frequency testbed, and the desired constellation diagram is obtained.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2022.3197812