2,595 research outputs found
Iterative pre-distortion of the non-linear satellite channel
Digital Video Broadcasting - Satellite - Second Generation (DVB-S2) is the
current European standard for satellite broadcast and broadband communications.
It relies on high order modulations up to 32-amplitude/phase-shift-keying
(APSK) in order to increase the system spectral efficiency. Unfortunately, as
the modulation order increases, the receiver becomes more sensitive to physical
layer impairments, and notably to the distortions induced by the power
amplifier and the channelizing filters aboard the satellite. Pre-distortion of
the non-linear satellite channel has been studied for many years. However, the
performance of existing pre-distortion algorithms generally becomes poor when
high-order modulations are used on a non-linear channel with a long memory. In
this paper, we investigate a new iterative method that pre-distorts blocks of
transmitted symbols so as to minimize the Euclidian distance between the
transmitted and received symbols. We also propose approximations to relax the
pre-distorter complexity while keeping its performance acceptable
Inference in Nonlinear Systems with Unscented Kalman Filters
An increasing number of scientific disciplines, most notably the life sciences and
health care, have become more quantitative, describing complex systems with coupled nonlinear
diâ”erential equations. While powerful algorithms for numerical simulations from these systems
have been developed, statistical inference of the system parameters is still a challenging problem.
A promising approach is based on the unscented Kalman filter (UKF), which has seen
a variety of recent applications, from soft tissue mechanics to chemical kinetics. The present
study investigates the dependence of the accuracy of parameter estimation on the initialisation.
Based on three toy systems that capture typical features of real-world complex systems: limit
cycles, chaotic attractors and intrinsic stochasticity, we carry out repeated simulations on a large
range of independent data instantiations. Our study allows a quantification of the accuracy of
inference, measured in terms of two alternative distance measures in function and parameter
space, in dependence on the initial deviation from the ground truth
Modeling and Compensation of Nonlinear Distortion in Horn Loudspeakers
Horn loaded compression drivers are widely used in the area where high sound pressure levels together with good directivity characteristics are needed. Major disadvantage of this kind of drivers is the considerable amount of nonlinear distortion. Due to the quite high air pressures in the driver the air is driven into its nonlinear range. This paper describes a technique to reduce the distortion caused by this phenomenon. Using a Digital Signal Processor (DSP), a feedforward compensation technique, based on an equivalent lumped parameter circuit, is implemented and tested in realâtime in series with the loudspeaker. Measurement and simulation results are given. The overall conclusion is that a distortion reduction is obtained in the frequency span from 600 to 1050 Hz
Versatile surrogate models for IC buffers
In previous papers [1,2] the authors have investigated the use of Volterra series in the identification of IC buffer macro-models. While the approach benefited from some of the inherent qualities of Volterra series it preserved the two-state paradigm of earlier methods (see [3] and its references) and was thus limited in its versatility. In the current paper the authors tackle the challenge of going beyond an application or device-oriented approach and build versatile surrogate models that mimic the behavior of IC buffers over a wide frequency band and for a variety of loads thus achieving an unprecedented degree of generality. This requires the use of a more general system identification paradig
Machine-learning nonstationary noise out of gravitational-wave detectors
Signal extraction out of background noise is a common challenge in high-precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal-to-noise ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is nonstationary, linear techniques often fail or are suboptimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove nonstationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational-wave observatory, where we could obtain an improvement of the detector gravitational-wave reach without introducing any bias on the source parameter estimation
Matrix Methods for the Dynamic Range Optimization of Continuous-TimeGm-CFilters
This paper presents a synthesis procedure for the optimization of the dynamic range of continuous-time fully differential G m - C filters. Such procedure builds up on a general extended state-space system representation which provides simple matrix algebra mechanisms to evaluate the noise and distortion performances of filters, as well as, the effect of amplitude and impedance scaling operations. Using these methods, an analytical technique for the dynamic range optimization of weakly nonlinear G m - C filters under power dissipation constraints is presented. The procedure is first explained for general filter structures and then illustrated with a simple biquadratic section
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