Model classification-and-selection assisted robust receiver for OFDM systems

Zhang, X., Mei, K., Liu, X., Zhang, L. and Wei, J. (2019) Model classification-and-selection assisted robust receiver for OFDM systems. IEEE Access, 7, pp. 85746-85754. (doi: 10.1109/ACCESS.2019.2924347)

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This paper devises a robust receiver for OFDM systems in the presence of residual timing offsets and unknown channel prior information. The proposed receiver constructs typical receiver models and resorts to the model selection technique to choose the best-matched receiver model to improve the channel estimation and signal detection. The typical receiver models are classified by considering the channel delay spread and the level of timing offset. Based on the receiver model selected by the Bayesian model selection algorithm, the channel length and timing offset parameters in the receiver model can provide the effective channel statistical information to make the channel estimator adapt to the altered circumstances and thus more accurate. Furthermore, the effective interference variance parameters in the selected receiver model are used to enhance the channel estimation and refine the soft symbol detection. The simulation results show that the proposed receiver achieves a significant performance gain compared to the existing methods. It is also shown that the proposed scheme makes the receiver robust to the diverse channel conditions and the timing offset states at a cost of the only a moderate increase in complexity.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Zhang, Professor Lei
Authors: Zhang, X., Mei, K., Liu, X., Zhang, L., and Wei, J.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Access
ISSN (Online):2169-3536
Published Online:21 June 2019
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in IEEE Access 7:85746-85754
Publisher Policy:Reproduced under a Creative Commons license

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