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Volterra series

About: Volterra series is a research topic. Over the lifetime, 2731 publications have been published within this topic receiving 46199 citations.


Papers
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Journal ArticleDOI
TL;DR: An important property of these Volterra filters is that they map sinusoidal inputs to constant outputs, which allows us to develop a new filter characterization that is more intuitive for the authors' application than the 4-D frequency response.
Abstract: An inherent problem in most image enhancement schemes is the amplification of noise, which, due to Weber's law, is mostly visible in the darker portions of an image. Using a special class of quadratic Volterra filters, we can adapt the enhancement process in a computationally efficient way to the local image brightness because these filters are approximately equivalent to the product of a local mean estimator and a highpass filter. We analyze and derive this subclass of quadratic Volterra filters by investigating the 1-D case first, and then we generalize the results to two dimensions. An important property of these filters is that they map sinusoidal inputs to constant outputs, which allows us to develop a new filter characterization that is more intuitive for our application than the 4-D frequency response. This description finally leads to a novel least-squares design methodology. Image enhancement results using our Volterra filters are superior to those obtained with standard linear filters, which we demonstrate both quantitatively and qualitatively.

76 citations

Journal ArticleDOI
TL;DR: In this article, a time-varying theory of Volterra series is developed and applied in the sampled data domain to solve for harmonic and intermodulation distortion of a MOS-based track-and-hold sampling mixer with a nonzero fall-time LO waveform.
Abstract: A time-varying theory of Volterra series is developed and applied in the sampled-data domain to solve for harmonic and intermodulation distortion of a MOS-based track-and-hold sampling mixer with a nonzero fall-time LO waveform. Distortion due to sampling error is also calculated. These results, when combined with the continuous-time solution, quantify harmonic and intermodulation distortion of a track-and-hold type mixer completely. Closed form solutions are obtained. As a practical consequence, it is shown that for certain fall-time, the distortion of track-and-hold mixers can be better than what would be predicted by a simple application of time-invariant Volterra series theory.

75 citations

Proceedings ArticleDOI
18 Apr 2011
TL;DR: In this paper, a simplified 2nd-order dynamic deviation reduction-based Volterra series model is proposed for characterizing wideband multi-carrier Doherty power amplifiers, which has much less complexity but maintains excellent modeling performance compared to the full-size model.
Abstract: A simplified 2nd-order dynamic deviation reduction-based Volterra series model is proposed for characterizing wideband multi-carrier Doherty power amplifiers. By removing several redundant terms, this model has much less complexity but maintains excellent modeling performance, compared to the full-size model. Experimental results show that both nonlinear distortion and memory effects in the Doherty PA can be almost completely removed by employing digital predistortion with proposed model, when excited by 2- and 4-carrier WCDMA signals.

75 citations

Journal ArticleDOI
TL;DR: The results show that the proposed C-BLRNN equalizer gives very favorable results in both the MSE and BER criteria over Volterra filter equalizer, DFE, and conventional complex-MLPNNequalizer.
Abstract: Equalization of satellite communication using complex-bilinear recurrent neural network (C-BLRNN) is proposed. Since the BLRNN is based on the bilinear polynomial, it can be used in modeling highly nonlinear systems with time-series characteristics more effectively than multilayer perceptron type neural networks (MLPNN). The BLRNN is first expanded to its complex value version (C-BLRNN) for dealing with the complex input values in the paper. C-BLRNN is then applied to equalization of a digital satellite communication channel for M-PSK and QAM, which has severe nonlinearity with memory due to traveling wave tube amplifier (TWTA). The proposed C-BLRNN equalizer for a channel model is compared with the currently used Volterra filter equalizer or decision feedback equalizer (DFE), and conventional complex-MLPNN equalizer. The results show that the proposed C-BLRNN equalizer gives very favorable results in both the MSE and BER criteria over Volterra filter equalizer, DFE, and complex-MLPNN equalizer.

74 citations

Proceedings ArticleDOI
02 Jun 2003
TL;DR: The results indicate that a multiple-point version of NORM can substantially reduce the model size and approach the ultimate model compactness that is achievable for nonlinear system reduction.
Abstract: This paper presents a compact Nonlinear model Order Reduction Method (NORM) that is applicable for time-invariant and time-varying weakly nonlinear systems. NORM is suitable for reducing a class of weakly nonlinear systems that can be well characterized by low order Volterra functional series. Unlike existing projection based reduction methods by J. Roychowdhury et al. (1999), NORM begins with the general matrix-form Volterra nonlinear transfer functions to derive a set of minimum Krylov subspaces for order reduction. Direct moment matching of the nonlinear transfer functions by projection of the original system onto this set of minimum Krylov subspaces leads to a significant reduction of model size. As we will demonstrate as part of our comparison with existing methods, the efficacy of model order for weakly nonlinear systems is determined by the extent to which models can be reduced. Our results further indicate that a multiple-point version of NORM can substantially reduce the model size and approach the ultimate model compactness that is achievable for nonlinear system reduction. We demonstrate the practical utility of NORM for macromodeling weakly nonlinear RF circuits with time-varying behavior.

74 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202315
202246
202146
202057
201983
201881