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A ML-Based High-Accuracy Estimation of Sampling and Carrier Frequency Offsets for OFDM Systems

  • Cang LiuEmail author
  • Luechao Yuan
  • Zuocheng Xing
  • Xiantuo Tang
  • Guitao Fu
Conference paper
  • 536 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 592)

Abstract

This paper addresses the problem of acquiring the sampling frequency offset (SFO) and carrier frequency offset (CFO), which severely degrade the performance of orthogonal frequency division multiplexing (OFDM) system. Using two identical frequency domain (FD) long training symbols in preamble, we propose a novel maximum-likelihood (ML) estimation method to simultaneously acquire the values of SFO and CFO, which extend the Kim’s and Wang’s estimation methods. The main contribution of this paper is that the first-order Legendre series expansion is used to obtain the SFO and CFO values in closed-form. For obtaining the performance of the proposed estimation scheme, we built the OFDM system model according to IEEE 802.11a. The results show that the proposed scheme achieves the best performance to the existing schemes.

Keywords

Sampling frequency offset Carrier frequency offset Orthogonal frequency division multiplexing Legendre series expansion 

Notes

Acknowledgments

This work is supported by National Science Foundation of China (Grant No. 61170083, 61373032) and Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20114307110001).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Cang Liu
    • 1
    Email author
  • Luechao Yuan
    • 1
  • Zuocheng Xing
    • 1
  • Xiantuo Tang
    • 2
  • Guitao Fu
    • 3
  1. 1.National Laboratory for Parallel and Distributed ProcessingNational University of Defense TechnologyChangshaChina
  2. 2.National Digital Switching System Engineering and Technological R&D CenterZhengzhouChina
  3. 3.Beijing Satellite Navigation Center (BSNC)BeijingChina

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