Deep Learning-Based Channel Estimation and Equalization Scheme for FBMC/OQAM Systems

Filter bank multicarrier (FBMC) modulation is a promising candidate modulation method for future communication systems. However, FBMC systems cannot directly use channel estimation methods proposed for orthogonal frequency-division multiplexing systems due to its inherent imaginary interference. In...

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Bibliographic Details
Published in:IEEE wireless communications letters 2019-06, Vol.8 (3), p.881-884
Main Authors: Cheng, Xing, Liu, Dejun, Wang, Chen, Yan, Song, Zhu, Zhengyu
Format: Article
Language:English
Subjects:
Artificial neural networks
Channel estimation
Communication channels
Computer simulation
Constellations
Deep learning
deep neural networks
Equalization
Filter bank multicarrier
Filter banks
Frequency division multiplexing
Interference
Machine learning
Modulation
Neurons
OFDM
OQAM
Orthogonal Frequency Division Multiplexing
Quadrature amplitude modulation
Training
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Summary:Filter bank multicarrier (FBMC) modulation is a promising candidate modulation method for future communication systems. However, FBMC systems cannot directly use channel estimation methods proposed for orthogonal frequency-division multiplexing systems due to its inherent imaginary interference. In this letter, we propose a channel estimation and equalization scheme based on deep learning (DL-CE) for FBMC systems. In the DL-CE scheme, the channel state information and the constellation demapping method are learned by a deep neural networks model, and then the distorted frequency-domain sequences are equalized implicitly to obtain binary bits directly. Simulation results show that the proposed DL-CE scheme achieves state-of-the-art performance on channel estimation and equalization.
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2019.2898437