RRQR based closed-form blind carrier offset estimator for OFDM systems

Loading...
Thumbnail Image
Authors
Qasaymeh, Mahmoud Mohammad
Nizar, T.
Hiren, G.
Sawan, M. Edwin
Pendse, Ravi
Advisors
Issue Date
2009-05-25
Type
Conference paper
Keywords
RRQRMethod , OFDM , Matrix Pencil Method , Estimation , DFT , Carrier Frequency Offset
Research Projects
Organizational Units
Journal Issue
Citation
Qasaymeh, M.M.; Nizar, T.; Hiren, G.; Sawan, M.E.; Pendse, R.; , "RRQR based closed-form blind carrier offset estimator for OFDM systems,"Telecommunications, 2009. ICT '09. International Conference on,pp.369-372, 25-27 May 2009 doi: 10.1109/ICTEL.2009.5158676
Abstract

Carrier Frequency Offset (CFO) is one of the major drawbacks of the Multi Carrier Modulation (MCM) technique. Blind, semiblind and data-aided techniques have been introduced to estimate the CFO. In this paper, a new blind CFO estimator based on the Matrix Pencil (MP) method is proposed. A closed-form solution of this generalized eigenvalue problem using the MP method is solved by Rank Revealing QR factorization (RRQR). The RRQR reveals information about numerical rank and efficiently divides the space into signal and noise subspaces. Our algorithm is proficient to estimate CFO by not involving Eigenvalue Decomposition (EVD) or Singular Value Decomposition (SVD) of the spectral data matrix. The proposed method is characterized by the highly accurate CFO estimation especially with a very small number of OFDM frames. Computer simulations are included to show the dominant performance of our proposed method by comparing it with the ESPRIT algorithm, particularly at lower numbers of available data blocks

Table of Contents
Description
The full text of this article is not available on SOAR. WSU users can access the article via IEEE Xplore database licensed by University Libraries: http://libcat.wichita.edu/vwebv/holdingsInfo?bibId=1045954
Publisher
IEEE
Journal
Book Title
Series
Telecommunications, 2009. ICT '09. International Conference on;pp.369-372
PubMed ID
DOI
ISSN
EISSN