Topic
Volterra series
About: Volterra series is a research topic. Over the lifetime, 2731 publications have been published within this topic receiving 46199 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this paper, an inter-subcarrier nonlinear and linear interference canceler (INIC) is proposed for long-haul Nyquist-wavelength division multiplexing superchannel transmission, which consists in detecting the adjacent subcarriers, regenerating them thanks to the Volterra series model of optical fiber, and removing them from the subcarrier of interest.
Abstract: For long-haul Nyquist-wavelength division multiplexing superchannel transmission, an inter-subcarrier nonlinear and linear interference canceler (INIC) is proposed. This approach consists in detecting the adjacent subcarriers, regenerating them thanks to the Volterra series model of optical fiber, and removing them from the subcarrier of interest. Different ways to implement the INIC are described and compared with the well-known techniques, such as digital backpropagation (DBP) and Volterra-based nonlinear equalizer (VNLE) implemented in a subcarrierwise manner. Significant performance gain (on either the $Q$ factor or transmission distance) is observed. In the context of 400 Gbps scheme, the transmission distance gain is up to 500 km compared with the DBP and VNLE.
10 citations
23 Nov 2009
TL;DR: This paper presents an efficient pseudorandom (PR) test method to characterize the performance of nonlinear analog and mixed-signal circuits including those embedded in SoC devices and presents a method to reduce the test time by using a compressed cross-correlation method which reduces the complexity of the presented algorithm.
Abstract: This paper presents an efficient pseudorandom (PR) test method to characterize the performance of nonlinear analog and mixed-signal (AMS) circuits including those embedded in SoC devices. Previous applications of the PR test method to BIST have been limited to digital and linear analog circuits. In this paper, we extend the application of PR test to nonlinear AMS circuits. In doing so, we reduce the cost of testing nonlinear circuits, and increase the test coverage of embedded AMS circuits without incurring a large area overhead to accommodate a test stimulus generator. Our method maintains good test accuracy by using a Volterra series model to describe the behavior of the device under test (DUT). A PR sequence generated from a simple LFSR is used to excite the DUTs over a wide range of frequencies and estimate the parameters of the Volterra series, which are then used to predict the performance of DUTs. We present a method to reduce the test time by using a compressed cross-correlation method which reduces the complexity of the presented algorithm. The mathematical background and hardware measurement results are presented to validate our method.
10 citations
TL;DR: This work accurately reconstructs the underlying Duffing oscillator by applying the method to time series from stochastic simulations, and introduces methods of statistical inference for the parameter values of the Stochastic Duffing model.
Abstract: The Duffing oscillator is a paradigm of bistable oscillatory motion in physics, engineering, and biology. Time series of such oscillations are often observed experimentally in a nonlinear system excited by a spontaneously fluctuating force. One is then interested in estimating effective parameter values of the stochastic Duffing model from these observations---a task that has not yielded to simple means of analysis. To this end we derive theoretical formulas for the statistics of the Duffing oscillator's time series. Expanding on our analytical results, we introduce methods of statistical inference for the parameter values of the stochastic Duffing model. By applying our method to time series from stochastic simulations, we accurately reconstruct the underlying Duffing oscillator. This approach is quite straightforward---similar techniques are used with linear Langevin models---and can be applied to time series of bistable oscillations that are frequently observed in experiments.
10 citations
23 Jun 2003
TL;DR: In this paper, a predictive control of nonlinear systems modelled by Volterra series with orthonormal basis functions expansion (Volterra-OBF) is proposed.
Abstract: This work focuses on predictive control of nonlinear systems modelled by Volterra series with orthonormal basis functions expansion (Volterra-OBF). By using Volterra series, any nonlinear analytical operator with finite memory can be approximated with an arbitrary precision. A Laguerre basis expansion is then used in the model parameterization to reduce the number of coefficients of the Volterra model. In this context, a predictive control with terminal constraints window based on cited model is proposed. Finally, simulated results using a CSTR unit illustrates the control scheme performance.
10 citations