NDLI: Adaptive blind equalization algorithm for QAM signals in OFDM systems by GI-restoration

Content Provider	IEEE Xplore Digital Library
Author	Alayyan, F.O. Zoubir, A.M. Yee Hong Leung
Copyright Year	2007
Description	Author affiliation: Etisalat Univ. Coll., Sharjah (Alayyan, F.O.)
Abstract	In this paper, a new blind equalization algorithm is proposed based on guard interval (GI)-restoral property for quadrature amplitude modulation (QAM) signals. The proposed algorithm utilizes existing redundancy in the time domain to completely eliminates inter-symbol-interference. We prove that the usage of GI-restoral property is sufficient to obtain a zero-forcing equalizer of the channel. The main advantage of this approach resides in its simplicity as the equalizer coefficients are estimated using a second-order least square fitting (LSF) scheme. The equalizer uses the LSF in the initial phase and then switches to a proposed constant modulus algorithm (CMA). The proposed CMA is constructed through the exploitation of a fully data information. To achieve a dramatic improvement in equalization performance, a modified CMA (MCMA) is proposed for OFDM systems based on cyclic prefixing approach. The resulting scheme provides a state-of-the-art low complexity with superior performance blind equalization. Extensive simulation shows that LSF-MCMA has a better steady-state equalization performance than LSF-CMA.
Starting Page	1
Ending Page	4
File Size	435936
Page Count	4
File Format	PDF
ISBN	9781424407781
DOI	10.1109/ISSPA.2007.4555590
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-02-12
Publisher Place	United Arab Emirates
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive equalizers Blind equalizers Quadrature amplitude modulation OFDM Signal processing algorithms Intersymbol interference Steady-state Redundancy Switches Convergence GI-restoral property CMA MCMA Adaptive blind equalization algorithm
Content Type	Text
Resource Type	Article

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