DocumentCode :
1987137
Title :
Neural network modeling and identification of nonlinear MIMO channels
Author :
Ibnkahla, M.
Author_Institution :
Electr. & Comput. Eng. Dept., Queens Univ., Kingston, ON
fYear :
2007
fDate :
12-15 Feb. 2007
Firstpage :
1
Lastpage :
4
Abstract :
The paper proposes a neural network (NN) approach for modeling and identification of a class of nonlinear multiple-input multiple-output (MIMO) channels. The unknown MIMO system is composed of a set of single-input memoryless nonlinearities followed by a linear combiner. The proposed NN model consists of a set of single-input memoryless NN blocks followed by an adaptive linear combiner. The performance of the proposed scheme is shown to outperform the classical multi-layer perceptron (MLP) in terms of convergence speed, mean squared error (MSE) and computational complexity. For uncorrelated inputs, the proposed NN structure enables the identification of each of the unknown nonlinearities as well as the combining matrix. Several simulation results and applications are presented in the paper, including tracking of slowly time-varying MIMO channels, and fault detection and characterization in nonlinear MIMO systems.
Keywords :
MIMO communication; computational complexity; mean square error methods; multilayer perceptrons; telecommunication computing; wireless channels; MLP; MSE; computational complexity; linear combiner; mean squared error; multilayer perceptron; multiple-input multiple-output channels; neural network modeling; nonlinear MIMO channels; Antennas and propagation; Computational complexity; Computational modeling; Computer networks; Convergence; MIMO; Multilayer perceptrons; Neural networks; Radio frequency; Transfer functions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on
Conference_Location :
Sharjah
Print_ISBN :
978-1-4244-0778-1
Electronic_ISBN :
978-1-4244-1779-8
Type :
conf
DOI :
10.1109/ISSPA.2007.4555423
Filename :
4555423
Link To Document :
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