DocumentCode :
1410637
Title :
Adaptive Multiregression in Reproducing Kernel Hilbert Spaces: The Multiaccess MIMO Channel Case
Author :
Slavakis, K. ; Bouboulis, P. ; Theodoridis, S.
Author_Institution :
Dept. of Telecommun. Sci. & Technol., Univ. of Peloponnese, Tripolis, Greece
Volume :
23
Issue :
2
fYear :
2012
Firstpage :
260
Lastpage :
276
Abstract :
This paper introduces a wide framework for online, i.e., time-adaptive, supervised multiregression tasks. The problem is formulated in a general infinite-dimensional reproducing kernel Hilbert space (RKHS). In this context, a fairly large number of nonlinear multiregression models fall as special cases, including the linear case. Any convex, continuous, and not necessarily differentiable function can be used as a loss function in order to quantify the disagreement between the output of the system and the desired response. The only requirement is the subgradient of the adopted loss function to be available in an analytic form. To this end, we demonstrate a way to calculate the subgradients of robust loss functions, suitable for the multiregression task. As it is by now well documented, when dealing with online schemes in RKHS, the memory keeps increasing with each iteration step. To attack this problem, a simple sparsification strategy is utilized, which leads to an algorithmic scheme of linear complexity with respect to the number of unknown parameters. A convergence analysis of the technique, based on arguments of convex analysis, is also provided. To demonstrate the capacity of the proposed method, the multiregressor is applied to the multiaccess multiple-input multiple-output channel equalization task for a setting with poor resources and nonavailable channel information. Numerical results verify the potential of the method, when its performance is compared with those of the state-of-the-art linear techniques, which, in contrast, use space-time coding, more antenna elements, as well as full channel information.
Keywords :
Hilbert spaces; MIMO communication; adaptive equalisers; convergence of numerical methods; learning (artificial intelligence); regression analysis; space-time codes; wireless channels; MIMO; RKHS; adaptive channel equalization; convergence analysis; convex analysis; linear complexity; linear techniques; multiple input multiple output; nonlinear multiregression models; reproducing kernel Hilbert space; robust loss functions; space-time coding; sparsiflcation strategy; Convex functions; Hilbert space; Kernel; Loss measurement; MIMO; Signal processing; Training data; Adaptive kernel learning; convex analysis; multiple-input multiple-output channel equalization; projection; regression; subgradient;
fLanguage :
English
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2162-237X
Type :
jour
DOI :
10.1109/TNNLS.2011.2178321
Filename :
6117088
Link To Document :
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