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Blind carrier frequency offset estimator for multi-input multi-output-orthogonal frequency division multiplexing systems over frequency-selective fading channels

Lookup NU author(s): Sedki Younis, Dr Arafat Al-Dweik, Professor Bayan Sharif, Dr Charalampos TsimenidisORCiD

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Abstract

This study presents a new blind carrier frequency offset (CFO) estimation technique for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems employing space-time coding (STC). CFO estimation is crucial for OFDM systems to avoid the performance degradation because of the inter-carrier interference that results when the CFO is not estimated and compensated accurately. Based on the assumptions that the data symbols are selected from a constant modulus constellation and the channel is varying slowly over time, a new blind CFO estimator is proposed by minimising the power difference between all subcarriers in two consecutive STC blocks. Therefore the proposed system exploits all subcarriers in time and frequency domain, which provides a remarkable performance improvement over other techniques reported in the literature. The complexity of the proposed estimator is substantially reduced by approximating the cost function by a sinusoid that can be minimised using direct closed-form computations within one OFDM symbol period. Monte Carlo simulations are used to assess the performance of the proposed system by means of mean squared error (MSE) in both static and time-varying frequency- selective fading channels. The simulation results demonstrate that the proposed estimator can eliminate the MSE error floors that usually appear at moderate and high signal-to-noise ratios for the estimators that work only in frequency domain.


Publication metadata

Author(s): Younis S, Al-Dweik A, Hazmi A, Sharif B, Tsimenidis C

Publication type: Article

Publication status: Published

Journal: IET Communications

Year: 2010

Volume: 4

Issue: 8

Pages: 990-999

Print publication date: 01/05/2010

ISSN (print): 1751-8628

ISSN (electronic): 1751-8636

Publisher: Institution of Engineering and Technology

URL: http://dx.doi.org/10.1049/iet-com.2009.0477

DOI: 10.1049/iet-com.2009.0477


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