A Gradient-Based Optimum Block Adaptation ICA Technique for Interference Suppression in Highly Dynamic Communication Cha
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A Gradient-Based Optimum Block Adaptation ICA Technique for Interference Suppression in Highly Dynamic Communication Channels Wasfy B. Mikhael1 and Tianyu Yang2 1 Department 2 Department
of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA of Engineering Sciences, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Received 21 February 2005; Revised 30 January 2006; Accepted 18 February 2006 The fast fixed-point independent component analysis (ICA) algorithm has been widely used in various applications because of its fast convergence and superior performance. However, in a highly dynamic environment, real-time adaptation is necessary to track the variations of the mixing matrix. In this scenario, the gradient-based online learning algorithm performs better, but its convergence is slow, and depends on a proper choice of convergence factor. This paper develops a gradient-based optimum block adaptive ICA algorithm (OBA/ICA) that combines the advantages of the two algorithms. Simulation results for telecommunication applications indicate that the resulting performance is superior under time-varying conditions, which is particularly useful in mobile communications. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
Independent component analysis (ICA) is a powerful statistical technique that has a wide range of applications. It has attracted huge research efforts in areas such as feature extraction [1], telecommunications [2–4], financial engineering [5], brain imaging [6], and text document analysis [7]. ICA can extract statistically independent components from a set of observations that are linear combinations of these components. The basic ICA model is X = AS. Here, X is the observation matrix, A is the mixing matrix, and S is the source signal matrix consisting of independent components. The objective of ICA is to find a separation matrix W, such that S can be recovered when the observation matrix X is multiplied by W. This is achieved by making each component in WX as independent as possible. Many principles and corresponding algorithms have been reported to accomplish this task, such as maximization of nongaussianity [8, 9], maximum likelihood estimation [10, 11], minimization of mutual information [12, 13], and tensorial methods [14–16]. The Newton-based fixed-point ICA algorithm [8], also known as the fast-ICA, is a highly efficient algorithm. It typically converges within less than ten iterations in a stationary environment. Moreover, in most cases the choice of the learning rate is avoided. However, when the mixing matrix is highly dynamic, fast-ICA cannot successfully track the time
variation. Thus, a gradient-based algorithm is more desirable in this scenario. The previously reported online gradient-based algorithm [17, page 177] suffers from slow convergence and difficulty in the choice of the learning rate. An improper choice of the learning rate, which is typically determined by trial and error, can result in slow con
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