Volterra series

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

SSBI Mitigation in A-RF-Tone-Based VSSB-OFDM System With a Frequency-Domain Volterra Series Equalizer

Abstract: The analog domain generated radio frequency-tone (A-RF-tone)-based virtual single sideband (VSSB) direct detection (DD) optical orthogonal frequency division multiplexing (OFDM) scheme is employed to eliminate the residual RF-tone image and substantially improve the signal quality after digital-to-analog converter. The VSSB signal is generated by electrically combining the baseband OFDM signals with sinusoidal waves without a frequency guard band to maximize the electrical and optical spectral efficiencies. The input–output relationship of the VSSB DD-OFDM system is represented and modeled as a second-order nonlinear kernel function by using the theory of Volterra series for the first time. The theory of Volterra inverse for the nonlinear system is introduced to analyze and mitigate subcarriers-to-subcarriers beating interference. In this paper, we further compare and analyze the inherent relationship between the iteration interference cancellation technique and Volterra postinverse equalizer, and that between symbol predistortion algorithm and Volterra preinverse equalizer. The analysis results show that the iteration interference cancellation technique can be considered as a second-order Volterra postinverse equalizer with iterative decision feedback, and symbol predistortion algorithm as a special designed second-order Volterra preinverse equalizer with iteration and step size control.

Volterra series and Picard iteration for nonlinear circuits and systems

Abstract: Two methods of analyzing nonlinear circuits and systems are iteration and Volterra series. This paper shows the relation between the two methods and gives a means of calculating Volterra kernels in the time domain.

Nonlinear systems analysis with non-Gaussian white stimuli; General basis functionals and kernels (Corresp.)

Abstract: The Wiener-Lee-Schetzen scheme of using Gaussian white noise to test a nonlinear dynamical system is extended in two ways 1) An arbitrary non-Ganssian white noise stationary signal can be used as the test stimulus 2) An arbitrary function of this stimulus can then be used as the analyzing function for cross correlating with the response to obtain the kernels characterizing the system Closed form expressions are given for the generalized orthogonal basis functions The generalized kernels are expanded in terms of Volterra kernels and Wiener kernels The expansion coefficients are closely related to the cumulants of the stimulus probability distribution These results are applied to the special case of a Gaussian stimulus and a three-level analysis function For this case a detailed analysis is Lade of the magnitude of the deviation of the kernels obtained with the ternary truncation as compared to the Wiener kernels obtained by cross correlating with the same Gaussian as was used for the stimulus The deviations are found to be quite small

Adaptive Volterra filters using orthogonal structures

Abstract: This paper presents an adaptive Volterra filter that employs a recently developed orthogonalization procedure of Gaussian signals for Volterra system identification. The algorithm is capable of handling arbitrary orders of nonlinearity P as well as arbitrary lengths of memory M for the system model. The adaptive filter consists of a linear lattice predictor of order N, a set of Gram-Schmidt orthogonalizers for N vectors of size P+1 elements each, and a joint process estimator in which each coefficient is adapted individually. The complexity of implementing this adaptive filter is comparable to the complexity of the system model when N is much larger than P, a condition that is true in many practical situations. Experimental results demonstrating the capabilities of the algorithm are also presented in the paper.

A Polynomial filtering model for enhancement of mammogram lesions

Abstract: This paper presents a preliminary analysis of a class of non-linear filters for enhancement of mammogram lesions. A non-linear filtering approach employing polynomial model of non-linearity is designed by second order truncation of Volterra series expansion. The proposed filter response is a linear combination of Type-0 and Type-II Volterra filters. Type-0 filter provides contrast enhancement, suppressing the ill-effects of background noise. On the other hand, Type-II filter employs edge enhancement. The objective analysis of the proposed filter is carried out by estimating values of quality parameters like CEM and PSNR on mammograms from MIAS and DDSM databases.