Methods for the automatic recognition of digital modulation of signals in cognitive radio systems

Adjemov, S. S.; Klenov, N. V.; Терешонок, М. В.; Chirov, D. S.

doi:10.3103/s0027134915060028

Cited by 19 publications

(12 citation statements)

References 15 publications

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“…In addition, adiabaticity conditions (Equation 12) should be satisfied. When these conditions are met, the following expression can be written to approximate the wave function: (13) and we can get the expression for the Wigner function: (14) where (15) Further we demonstrate two effects in this approximation:…”

Section: Dynamics Of the Quantum Neuron Without Dissipationmentioning

confidence: 89%

A superconducting adiabatic neuron in a quantum regime

Bastrakova¹,

Pashin²,

Rybin³

et al. 2022

Beilstein J. Nanotechnol.

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We explore the dynamics of an adiabatic neural cell of a perceptron artificial neural network in a quantum regime. This mode of cell operation is assumed for a hybrid system of a classical neural network whose configuration is dynamically adjusted by a quantum co-processor. Analytical and numerical studies take into account non-adiabatic processes as well as dissipation, which leads to smoothing of quantum coherent oscillations. The obtained results indicate the conditions under which the neuron possesses the required sigmoid activation function.

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Section: Dynamics Of the Quantum Neuron Without Dissipationmentioning

confidence: 89%

A superconducting adiabatic neuron in a quantum regime

Bastrakova¹,

Pashin²,

Rybin³

et al. 2022

Beilstein J. Nanotechnol.

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“…A simple way to do this is to raise the signal to the appropriate power. Let, for example, the analyzed signal is described by expression (1), where in the case of BPSK signal processing θ = {0, π}. Let's square this signal…”

Section: Methods For Recognizing the Types Of Signal Modulationmentioning

confidence: 99%

“…There are many algorithms for determining signal parameters. For example, in [1,2], a method is proposed for recognizing the type of modulation by the signal constellation. The reason for the shortcomings of this recognition method is low information content, the probability of correct recognition strongly depends on the signal-to-noise ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

Cumulative features for determining the type of signal manipulation

Kurbanaliev¹

2020

RENSIT

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This paper describes the apparatus of cumulant analysis in relation to the problem of recognizing the types of signal modulation. The article presents the results of using artificial neural networks in the task of automating the detection of intra-pulse modulation signs for the identification (classification) of signals. A mathematical model of a phase-shift keyed signal is developed, the main properties of this type of signals are described and a method is proposed that allows one to determine the type of signal manipulation based on the calculation of informative (cumulative) features. Simulation was carried out in Matlab/Simulink.

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“…MLP is the most frequently used for its solution. However, this type of neural network does not provide a probabilistic interpretation of the classification results and requires rather lengthy training [5–6]. An RBF-based network or a probabilistic network lack these disadvantages.…”

Section: Gauss Cell: the Basic Element For A Probabilistic Networkmentioning

confidence: 99%