Results 1  10
of
16
ComplexValued Neural Networks with Adaptive Spline Activation Function for Digital Radio Links Nonlinear Equalization
 IEEE TRANS. ON SIGNAL PROCESSING
, 1999
"... In this paper, a new complexvalued neural network based on adaptive activation functions is proposed. By varying the control points of a pair of Catmull–Rom cubic splines, which are used as an adaptable activation function, this new kind of neural network can be implemented as a very simple structu ..."
Abstract

Cited by 27 (16 self)
 Add to MetaCart
(Show Context)
In this paper, a new complexvalued neural network based on adaptive activation functions is proposed. By varying the control points of a pair of Catmull–Rom cubic splines, which are used as an adaptable activation function, this new kind of neural network can be implemented as a very simple structure that is able to improve the generalization capabilities using few training samples. Due to its low architectural complexity (low overhead with respect to a simple FIR filter), this network can be used to cope with several nonlinear DSP problems at a high symbol rate.
In particular, this work addresses the problem of nonlinear channel equalization. In fact, although several authors have already recognized the usefulness of a neural network as a channel equalizer, one problem has not yet been addressed: the high complexity and the very long data sequence needed to train the network. Several experimental results using a realistic channel model are reported that prove the effectiveness of the proposed network on equalizing a digital satellite radio link in the presence of noise, nonlinearities, and intersymbol interference (ISI).
On the Approximation of Correlated NonGaussian Noise Pdfs using Gaussian Mixture Models
 American University, Washington DC
, 1999
"... Gaussian mixture probability density functions (pdfs) have been popular for modeling nonGaussian noise. The majority of nonGaussian noise research has been restricted to independent and identically distributed observation sequences due to the difficulty in characterizing multidimensional pdf&apo ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
(Show Context)
Gaussian mixture probability density functions (pdfs) have been popular for modeling nonGaussian noise. The majority of nonGaussian noise research has been restricted to independent and identically distributed observation sequences due to the difficulty in characterizing multidimensional pdf's. There has been very few studies on the ability of Gaussian mixture pdfs to model correlated nonGaussian noise processes. In this paper, we initiate such a study and demonstrate that in practical cases, Gaussian mixture pdfs with a small number of mixing terms can give good approximations to nonGaussian noise pdfs. Some general models for correlated nonGaussian interference and noise are reviewed. The focus is on three approaches. The first is the Gaussian mixture model approach. The second is an approach based on spherically invariant random vectors. The final approach involves the combination of linear filters and nonlinearities, generally in an adhoc manner. The three approaches...
OnLine Adaptation of a Signal Predistorter through Dual Reinforcement Learning
, 1996
"... Several researchers have demonstrated how neural networks can be trained to compensate for nonlinear signal distortion in e.g. digital satellite communications systems. These networks, however, require that both the original signal and its distorted version are known. Therefore, they have to be trai ..."
Abstract

Cited by 5 (3 self)
 Add to MetaCart
(Show Context)
Several researchers have demonstrated how neural networks can be trained to compensate for nonlinear signal distortion in e.g. digital satellite communications systems. These networks, however, require that both the original signal and its distorted version are known. Therefore, they have to be trained offline, and they cannot adapt to changing channel characteristics. In this paper, a novel dual reinforcement learning approach is proposed that can adapt online while the system is performing. Assuming that the channel characteristics are the same in both directions, two predistorters at each end of the communication channel coadapt using the output of the other predistorter to determine their own reinforcement. Using the common Volterra Series model to simulate the channel, the system is shown to successfully learn to compensate for distortions up to 30%, which is significantly higher than what might be expected in an actual channel. 1 Introduction In a satellite communication sys...
GCMACbased predistortion for digital modulations
 IEEE Trans. on Communications
, 2001
"... ..."
(Show Context)
Adaptive signal processing: A discussion of tradeoffs from the perspective of artificial learning
 in Proc. 1996 European Conf. on Signal Processing
, 1996
"... Since many signal processing problems can be posed as samplebased decision and estimation tasks, we discuss how studies from other fields such as neural networks might suggest improved architectures (models) and algorithms for these types of problems. We then concentrate on PAM equalization, showin ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Since many signal processing problems can be posed as samplebased decision and estimation tasks, we discuss how studies from other fields such as neural networks might suggest improved architectures (models) and algorithms for these types of problems. We then concentrate on PAM equalization, showing that a reordering of the equivalent classification problem generates a 'staircase ' which, while retaining the simplicity of the classical equalizer, allows modifications to made in the outputs and in the training objectives which provide advantages even in the least complex cases.We go on to demonstrate that these advantages increase when one considers, for example, nonlinear channels with memory. From our simulations we draw conclusions and suggest futher related research. We also present two new avenues of inquiry, offering significant practical advantages, which are motivated by the discussions. 1.
RESTORATION OF NONLINEARLY DISTORTED OPTICAL SOUNDTRACKS USING REGULARIZED INVERSE CHARACTERISTICS
, 2004
"... ..."
Copyright
, 2005
"... The dissertation of Steve C. Thompson is approved, and it is acceptable in quality and form for publication on microfilm: ..."
Abstract
 Add to MetaCart
The dissertation of Steve C. Thompson is approved, and it is acceptable in quality and form for publication on microfilm:
Performance of Volterra and MLSD Receivers for Nonlinear Bandlimited Satellite Systems
, 1997
"... This paper evaluates the performance of receiverbased compensation methods for distortion caused by nonlinear bandlimited satellite channels. Speci cally, the performance of Volterra equalizers and maximumlikelihood sequence detection (MLSD) receivers for nonlinear bandlimited satellite systems a ..."
Abstract
 Add to MetaCart
(Show Context)
This paper evaluates the performance of receiverbased compensation methods for distortion caused by nonlinear bandlimited satellite channels. Speci cally, the performance of Volterra equalizers and maximumlikelihood sequence detection (MLSD) receivers for nonlinear bandlimited satellite systems are quanti ed. In addition, the performance of a receiver with a fractionalyspaced equalizer followed by a Volterra equalizer is studied (FSEVolterra equalizer). We use as our performance criteria meansquare error (MSE) and probability of error. For the equalizers, adaption of the equalizer weights is discussed as well as a multiplestep LMS algorithm which improves the convergence characteristics. Two MLSD receiver structures are considered, and the performance is compared to that obtained by the equalizers. The optimum receiver for a nonlinear bandlimited satellite channel is a matchedlter bank followed by a Viterbi detector; this is the socalled MFBMLSD receiver. Because the number ...
EURASIP Journal on Applied Signal Processing 2003:12, 1219–1228 c ○ 2003 Hindawi Publishing Corporation Modeling Nonlinear Power Amplifiers in OFDM Systems from Subsampled Data: A Comparative Study Using Real Measurements
, 2003
"... A comparative study among several nonlinear highpower amplifier (HPA) models using real measurements is carried out. The analysis is focused on specific models for wideband OFDM signals, which are known to be very sensitive to nonlinear distortion. Moreover, unlike conventional techniques, which ty ..."
Abstract
 Add to MetaCart
A comparative study among several nonlinear highpower amplifier (HPA) models using real measurements is carried out. The analysis is focused on specific models for wideband OFDM signals, which are known to be very sensitive to nonlinear distortion. Moreover, unlike conventional techniques, which typically use a singletone test signal and power measurements, in this study the models are fitted using subsampled timedomain data. The inband and outofband (spectral regrowth) performances of the following models are evaluated and compared: Saleh’s model, envelope polynomial model (EPM), Volterra model, the multilayer perceptron (MLP) model, and the smoothed piecewiselinear (SPWL) model. The study shows that the SPWL model provides the best inband characterization of the HPA. On the other hand, the Volterra model provides a good tradeoff between model complexity (number of parameters) and performance.
Fourier Models For NonLinear Signal Processing
, 1999
"... This paper proposes a trigonometric functional extension, hereafter named the Fourier model, as an alternative framework to the Volterra approach for nonlinear systems modelling. This work is focused on the general advantages that trigonometric functionals show in adaptive implementations and also ..."
Abstract
 Add to MetaCart
This paper proposes a trigonometric functional extension, hereafter named the Fourier model, as an alternative framework to the Volterra approach for nonlinear systems modelling. This work is focused on the general advantages that trigonometric functionals show in adaptive implementations and also on the possibility they provide to reuse wellknown linear processing tools in a nonlinear context. The performance of the Fourier model is compared in a set of simulations that cover companders for audio and radio frequency amplifiers, Probability Density Function (PDF) whitening and PDF estimation.