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Báo cáo hóa học: " Blind Adaptive Channel Equalization with Performance Analysis"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Blind Adaptive Channel Equalization with Performance Analysis | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article iD 72879 Pages 1-9 DOI 10.1155 ASP 2006 72879 Blind Adaptive Channel Equalization with Performance Analysis Shiann-Jeng Yu1 and Fang-Biau Ueng2 1 National Center for High Performance Computing No. 21 Nan-Ke 3rd Road Hsin-Shi Tainan County 744 Taiwan 2 Department of Electrical Engineering National Chung-Hsing University 250 Kuo-Kuang Road Taichung 402 Taiwan Received 4 March 2005 Revised 25 August 2005 Accepted 26 September 2005 Recommended for Publication by Christoph Mecklenbrauker A new adaptive multiple-shift correlation MSC -based blind channel equalizer BCE for multiple FIR channels is proposed. The performance of the MSC-based BCE under channel order mismatches due to small head and tail channel coefficient is investigated. The performance degradation is a function of the optimal output SINR the optimal output power and the control vector. This paper also proposes a simple but effective iterative method to improve the performance. Simulation examples are demonstrated to show the effectiveness of the proposed method and the analyses. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Traditional adaptive equalizers are based on the periodic transmission of a known training data sequence in order to identify or equalize a distorted channel with intersymbol interference ISI . However the use of training data sequence may be very costly in some applications. Blind channel equalizers BCE without training data available receive much attention in recent years 1-15 . Early blind equalization techniques 1 2 exploited the higher order statistics HOS of the output to identify the channels. Unfortunately the HOS-based BCE requires a large number of data samples and huge computation load which limit their applications in fast changing environments. To circumvent the shortcomings of the HOS-based approaches second-order statistics SOS was .