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  • Research Article
  • Open Access

Non-Pilot-Aided Sequential Monte Carlo Method to Joint Signal, Phase Noise, and Frequency Offset Estimation in Multicarrier Systems

  • 1Email author,
  • 2,
  • 1 and
  • 2
EURASIP Journal on Advances in Signal Processing20082008:612929

https://doi.org/10.1155/2008/612929

  • Received: 27 July 2007
  • Accepted: 2 April 2008
  • Published:

Abstract

We address the problem of phase noise (PHN) and carrier frequency offset (CFO) mitigation in multicarrier receivers. In multicarrier systems, phase distortions cause two effects: the common phase error (CPE) and the intercarrier interference (ICI) which severely degrade the accuracy of the symbol detection stage. Here, we propose a non-pilot-aided scheme to jointly estimate PHN, CFO, and multicarrier signal in time domain. Unlike existing methods, non-pilot-based estimation is performed without any decision-directed scheme. Our approach to the problem is based on Bayesian estimation using sequential Monte Carlo filtering commonly referred to as particle filtering. The particle filter is efficiently implemented by combining the principles of the Rao-Blackwellization technique and an approximate optimal importance function for phase distortion sampling. Moreover, in order to fully benefit from time-domain processing, we propose a multicarrier signal model which includes the redundancy information induced by the cyclic prefix, thus leading to a significant performance improvement. Simulation results are provided in terms of bit error rate (BER) and mean square error (MSE) to illustrate the efficiency and the robustness of the proposed algorithm.

Keywords

  • Mean Square Error
  • Phase Noise
  • Carrier Frequency Offset
  • Cyclic Prefix
  • Phase Distortion

Publisher note

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Authors’ Affiliations

(1)
IEMN-DOAE UMR 8520, UVHC Le Mont Houy, 59313 Valenciennes Cedex 9, France
(2)
GET/INT/Telecom Lille 1, 59658 Villeneuve d'Ascq, France

Copyright

© François Septier et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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