TAILIEUCHUNG - Báo cáo sinh học: " Research Article Maximum-Likelihood Semiblind Equalization of Doubly Selective Channels Using the EM Algorithm"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Maximum-Likelihood Semiblind Equalization of Doubly Selective Channels Using the EM Algorithm | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 709143 14 pages doi 2010 709143 Research Article Maximum-Likelihood Semiblind Equalization of Doubly Selective Channels Using the EM Algorithm Gideon Kutz and Dan Raphaeli Faculty of Engineering Systems Tel-Aviv University Tel-Aviv 66978 Israel Correspondence should be addressed to Gideon Kutz Received 5 August 2009 Revised 16 April 2010 Accepted 9 June 2010 Academic Editor Cihan Tepedelenlioglu Copyright 2010 G. Kutz and D. Raphaeli. 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. Maximum-likelihood semi-blind joint channel estimation and equalization for doubly selective channels and single-carrier systems is proposed. We model the doubly selective channel as an FIR filter where each filter tap is modeled as a linear combination of basis functions. This channel description is then integrated in an iterative scheme based on the expectation-maximization EM principle that converges to the channel description vector estimation. We discuss the selection of the basis functions and compare various functions sets. To alleviate the problem of convergence to a local maximum we propose an initialization scheme to the EM iterations based on a small number of pilot symbols. We further derive a pilot positioning scheme targeted to reduce the probability of convergence to a local maximum. Our pilot positioning analysis reveals that for high Doppler rates it is better to spread the pilots evenly throughout the data block and not to group them even for frequency-selective channels. The resulting equalization algorithm is shown to be superior over previously proposed equalization schemes and to perform in many cases close to the maximum-likelihood equalizer with perfect channel .

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