Topic
Volterra series
About: Volterra series is a research topic. Over the lifetime, 2731 publications have been published within this topic receiving 46199 citations.
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TL;DR: In this paper, the authors determine and compare the computational complexity of two such compensators: one based on the analytical pth order Volterra inverse, the other on the adaptive Volterras inverse.
Abstract: The Volterra series can be used to represent a wide class of nonlinear systems with memory. A Volterra inverse can be used to apply post (or pre)-distortion for the purpose of nonlinear compensation. The authors determine and compare the computational complexity of two such compensators: one based on the analytical pth order Volterra inverse, the other on the adaptive Volterra inverse.
63 citations
TL;DR: In this paper, the state space of a finite Volterra series is shown to have the homogeneous space structure of a nilmanifold, the quotient of two nilpotent Lie groups.
Abstract: In this paper, realizations of finite Volterra series are viewed as nonlinear analytic input–output systems, with state space described by an analytic manifold. For a minimal realization guaranteed by H. J. Sussmann, the state space, which is unique up to diffeomorphism, is shown to have the homogeneous space structure of a nilmanifold, the quotient of two nilpotent Lie groups. The structure of nilmanifolds as described by A. Malcev is used to show that for these systems, the state space has a vector space structure. As a consequence of this result, it is shown that a minimal realization of a finite Volterra series can be described as a cascade of linear subsystems with polynomial link maps, in which the dimension o f each linear subsystem is independent of the realization considered.
62 citations
TL;DR: In this article, a low-frequency-trap network is added to the base of an inductively-degenerated common-emitter transconductance stage to improve its third-order intercept point, but not its 1-dB compression point.
Abstract: It is well known that a low-frequency-trap network can be added to the base of an inductively-degenerated common-emitter transconductance stage to improve its third-order intercept point, but not its 1-dB compression point. High-frequency equations in Volterra series are used to explain this phenomenon. Analytical and experimental results show that the third-order intercept point increases with the capacitance within the low-frequency-trap network.
62 citations
TL;DR: A digital correction algorithm for the dynamic errors generated in this stage of the track-and-hold stage of high-speed, high-resolution ADCs is proposed and it is shown that the number of coefficients required in the model is significantly smaller than thenumber of coefficients in the general form of the Volterra series.
Abstract: The track-and-hold stage at the front-end of high-speed, high-resolution ADCs is usually the limiting factor in their linearity performance at high input frequencies. In this paper, we propose a digital correction algorithm for the dynamic errors generated in this stage. The digital post-processing scheme uses circuit insight and judicious modeling of the relevant nonidealities to minimize complexity. We show that the number of coefficients required in our model is significantly smaller than the number of coefficients in the general form of the Volterra series. The coefficients are extracted in a foreground calibration approach using least square (LS) solutions on a set of input and output samples from training signals. Simulation results on a nonlinear track-and-hold circuit model show approximately 40 dB of improvement in linearity (SFDR) until the fourth Nyquist zone (at fs = 100 MHz). The method was also applied to a commercially available 14-bit, 155-MS/s ADC and showed to improve its SFDR to more than 83 dB up to an input frequency of 470 MHz in a lab experiment.
62 citations
TL;DR: This study presented an efficient adaptive failure detection mechanism based on volterra series, which can use a small amount of data for predicting and uses avolterra filter for time series prediction and a decision tree for decision making.
Abstract: Failure detection module is one of important components in fault-tolerant distributed systems, especially cloud platform. However, to achieve fast and accurate detection of failure becomes more and more difficult especially when network and other resources' status keep changing. This study presented an efficient adaptive failure detection mechanism based on volterra series, which can use a small amount of data for predicting. The mechanism uses a volterra filter for time series prediction and a decision tree for decision making. Major contributions are applying volterra filter in cloud failure prediction, and introducing a user factor for different QoS requirements in different modules and levels of IaaS. Detailed implementation is proposed, and an evaluation is performed in Beijing and Guangzhou experiment environment.
61 citations