A General Theory of Intelligible Crosstalk between Frequency-Division Multiplexed Angle-Modulated Carriers

Shimbo, O.; Pontano, B. A.

doi:10.1109/tcom.1976.1093422

Cited by 15 publications

(2 citation statements)

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“…The BP algorithm is a supervised learning procedure that uses the measured TWT input and output signals to iteratively adjust the neural network weights. The simulation results indicated that the MLNN approach performs better than classical approximation techniques [2], [5], [9], [13], [14], [16].…”

Section: Introductionmentioning

confidence: 98%

Statistical analysis of a two-layer backpropagation algorithm used for modeling nonlinear memoryless channels: the single neuron case

Bershad

Ibnkahla

Castanié

1997

IEEE Trans. Signal Process.

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Neural networks have been used for modeling the nonlinear characteristics of memoryless nonlinear channels using backpropagation (BP) learning with experimental training data. In order to better understand this neural network application, this paper studies the transient and convergence properties of a simplified two-layer neural network that uses the BP algorithm and is trained with zero mean Gaussian data. The paper studies the effects of the neural net structure, weights, initial conditions, and algorithm step size on the mean square error (MSE) of the neural net approximation. The performance analysis is based on the derivation of recursions for the mean weight update that can be used to predict the weights and the MSE over time. Monte Carlo simulations display good to excellent agreement between the actual behavior and the predictions of the theoretical model.

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Section: Introductionmentioning

confidence: 98%

Statistical analysis of a two-layer backpropagation algorithm used for modeling nonlinear memoryless channels: the single neuron case

Bershad

Ibnkahla

Castanié

1997

IEEE Trans. Signal Process.

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“…The computational complexity of these neural net architectures (order of 15 scalar parameters) is comparable to some classical models (such as the Shimbo-Pontano model [13]), and is greater than others (e.g., the Saleh model [12]). The neural net can be implemented in parallel, resulting in a large reduction of the computation time (for both learning and generalization).…”

Section: Comments On Neural Network Modelsmentioning

confidence: 98%

Neural networks for modeling nonlinear memoryless communication channels

Ibukahla

Sombria²,

Castanié

et al. 1997

IEEE Trans. Commun.

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This paper presents a neural network approach for modeling nonlinear memoryless communication channels. In particular, the paper studies the approximation of the nonlinear characteristics of traveling-wave tube (TWT) amplifiers used in satellite communications. The modeling is based upon multilayer neural networks, trained by the odd and even backpropagation (BP) algorithms. Simulation results demonstrate that neural network models fit the experimental data better than classical analytical TWT models. Index Terms-Neural networks, satellite communications, TWT amplifiers. I. INTRODUCTION S EVERAL nonlinear channels (e.g., satellite communication channels) [1] are equipped with memoryless nonlinear devices such as traveling-wave tube (TWT) amplifiers. These devices exhibit two kinds of nonlinearities, amplitude distortion (AM/AM conversion) and phase distortion (AM/PM conversion). Two equivalent frequency-independent representations have been proposed for these nonlinearities [1], [12]. These representations are amplitude-phase (A-P) and in-phase and quadrature (I-Q). Several analytical models such as Bessel and rational functions have been proposed for these nonlinear distortions. This paper adaptively models these nonlinear functions using the odd and even backpropagation (BP) algorithms [9]. These supervised learning procedures use the measured TWT input and output signals for iteratively adjusting the neural network weights. The simulation results indicate that the neural net approach performs better than classical approximation techniques. Furthermore, a change in the TWT characteristic can be tracked by the neural net since the net is adaptive. This yields a new model which has adapted to this change. The neural net can approximate all types of TWT's with the same basic structure (only the weights change). The neural architecture has few parameters (e.g., only 15 scalar parameters are needed to model the AM/AM conversion of an Intelsat TWT amplifier). Moreover, this architecture can be implemented rapidly in parallel for both the learning and generalization phases. This paper focuses Paper approved by P. H. Wittke, the Editor for Communication Theory of the IEEE Communications Society.

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Neural Networks for Communications and Signal Processing: Overview and New Results

Ibnkahla

Castanié

1996

Information Technology: Transmission, Processing and Storage

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A General Theory of Intelligible Crosstalk between Frequency-Division Multiplexed Angle-Modulated Carriers

Cited by 15 publications

References 0 publications

Statistical analysis of a two-layer backpropagation algorithm used for modeling nonlinear memoryless channels: the single neuron case

Statistical analysis of a two-layer backpropagation algorithm used for modeling nonlinear memoryless channels: the single neuron case

Neural networks for modeling nonlinear memoryless communication channels

Neural Networks for Communications and Signal Processing: Overview and New Results

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