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Maximum-likelihood estimator for coarse carrier frequency offset estimation in OFDM systems

Maximum-likelihood estimator for coarse carrier frequency offset estimation in OFDM systems

Yang, Feng, Zhang, Zhenrong and Chen, Yifan (2009) Maximum-likelihood estimator for coarse carrier frequency offset estimation in OFDM systems. Wireless Personal Communications, 49 (1). pp. 55-66. ISSN 0929-6212 (Print), 1572-834X (Online) (doi:10.1007/s11277-008-9555-5)

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Abstract

Orthogonal frequency division multiplexing (OFDM) systems are more sensitive to carrier frequency offset (CFO) compared to the conventional single carrier systems. CFO destroys the orthogonality among subcarriers, resulting in inter-carrier interference (ICI) and degrading system performance. To mitigate the effect of the CFO, it has to be estimated and compensated before the demodulation. The CFO can be divided into an integer part and a fractional part. In this paper, we investigate a maximum-likelihood estimator (MLE) for estimating the integer part of the CFO in OFDM systems, which requires only one OFDM block as the pilot symbols. To reduce the computational complexity of the MLE and improve the bandwidth efficiency, a suboptimum estimator (Sub MLE) is studied. Based on the hypothesis testing method, a threshold Sub MLE (T-Sub MLE) is proposed to further reduce the computational complexity. The performance analysis of the proposed T-Sub MLE is obtained and the analytical results match the simulation results well. Numerical results show that the proposed estimators are effective and reliable in both additive white Gaussian noise (AWGN) and frequency-selective fading channels in OFDM systems.

Item Type: Article
Uncontrolled Keywords: carrier frequency offset (CFO), OFDM, coarse estimation, maximum-likelihood estimation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Engineering & Science
Related URLs:
Last Modified: 14 Oct 2016 09:04
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/1622

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