Comparative Performance of Complex-Valued B-Spline and Polynomial Models Applied to Iterative Frequency-Domain Decision Feedback Equalization of Hammerstein Channels

Complex-valued (CV) B-spline neural network approach offers a highly effective means for identifying and inverting practical Hammerstein systems. Compared with its conventional CV polynomial-based counterpart, a CV B-spline neural network has superior performance in identifying and inverting CV Hamm...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2017-12, Vol.28 (12), p.2872-2884
Main Authors: Sheng Chen, Xia Hong, Khalaf, Emad F., Alsaadi, Fuad E., Harris, Chris J.
Format: Article
Language:English
Subjects:
Channels
Complex-valued (CV) polynomial model
Complexity
Complexity theory
Computational modeling
CV B-spline neural network
Decision feedback equalizers
Equalization
Feedback
Frequency-domain analysis
identification and inversion of Hammerstein channels
Iterative methods
Neural networks
nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE)
Polynomials
Splines (mathematics)
System effectiveness
Transmitters
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Summary:Complex-valued (CV) B-spline neural network approach offers a highly effective means for identifying and inverting practical Hammerstein systems. Compared with its conventional CV polynomial-based counterpart, a CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. This paper reviews the optimality of the CV B-spline neural network approach. Advantages of B-spline neural network approach as compared with the polynomial based modeling approach are extensively discussed, and the effectiveness of the CV neural network-based approach is demonstrated in a real-world application. More specifically, we evaluate the comparative performance of the CV B-spline and polynomial-based approaches for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Our results confirm the superior performance of the CV B-spline-based NIFDDFE over its CV polynomial-based counterpart.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2016.2609001