Deep OFDM Channel Estimation: Capturing Frequency Recurrence

In this letter, we propose a deep-learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. Our proposed method, named Single Slot Recurrence Along Frequency Network (SisRafNet), is based on a novel study of recurrent models for exploiting sequential be...

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Bibliographic Details
Published in:IEEE communications letters 2024-03, Vol.28 (3), p.562-566
Main Authors: Jameel, Abu Shafin Mohammad Mahdee, Malhotra, Akshay, Gamal, Aly El, Hamidi-Rad, Shahab
Format: Article
Language:English
Subjects:
bidirectional RNN for wireless
Channel estimation
Channels
Convolutional neural networks
Correlation
Deep learning
Deep learning for channel estimation
Frequency estimation
frequency recurrence
Network latency
OFDM
Orthogonal Frequency Division Multiplexing
Recurrent neural networks
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Summary:In this letter, we propose a deep-learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. Our proposed method, named Single Slot Recurrence Along Frequency Network (SisRafNet), is based on a novel study of recurrent models for exploiting sequential behavior of channels across frequencies. Utilizing the fact that wireless channels have a high degree of correlation across frequencies, we employ recurrent neural network techniques within a single OFDM slot, thus overcoming the latency and memory constraints typically associated with recurrence based methods. The proposed SisRafNet delivers superior estimation performance compared to existing deep-learning-based channel estimation techniques and the performance has been validated on a wide range of 3rd Generation Partnership Project (3GPP) compliant channel scenarios at multiple signal-to-noise ratios.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2024.3350369