title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ñ Ð°Ð½Ð°Ð»Ð¾Ð³Ð¾Ð²Ð¾Ð¹ Ð¸ ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð¹ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸ÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Application of convolutional neural networks for the classification of signals with analog and digital modulation
Creators: name: Ð Ð¾Ð¼ÐµÐ½ÑÐºÐ°Ñ, ÐÐ°ÑÐ³Ð°ÑÐ¸ÑÐ°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
ÑÐ¸ÑÑÐ¾Ð²Ð°Ñ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ñ
Ð°Ð½Ð°Ð»Ð¾Ð³Ð¾Ð²Ð°Ñ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ñ
ÑÑÐ½ÐºÑÐ¸Ñ Ð¿ÑÐ°Ð²Ð´Ð¾Ð¿Ð¾Ð´Ð¾Ð±Ð¸Ñ
Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸ ÑÐ¸Ð³Ð½Ð°Ð»Ð°
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
recognition
classification
digital modulation
analog modulation
likelihood function
signal features
artificial neural networks
deep learning

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ñ Ð°Ð½Ð°Ð»Ð¾Ð³Ð¾Ð²Ð¾Ð¹ Ð¸ ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð¹ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸ÐµÐ¹. ÐÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½ Ð¾Ð±Ð·Ð¾Ñ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ¸Ð¿Ð¾Ð² Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¸. ÐÑÐ¼ÐµÑÐ°ÑÑÑÑ Ð´Ð¾ÑÑÐ¾Ð¸Ð½ÑÑÐ²Ð° Ð¸ Ð½ÐµÐ´Ð¾ÑÑÐ°ÑÐºÐ¸ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÐ¼ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð². ÐÐ¿Ð¸ÑÐ°Ð½Ñ ÑÑÑÑÐºÑÑÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ ÑÐµÑÐ°ÑÑÐµÐ¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ ÑÐ²ÑÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ. ÐÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð° Ð¸Ð¼Ð¸ÑÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ. ÐÑÐ±ÑÐ°Ð½Ð° ÑÐµÐ»ÐµÐ²Ð°Ñ ÑÑÐ½ÐºÑÐ¸Ñ Ð¸ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÐµ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ°. Ð Ð°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð¾ ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸ ÑÑÐ½ÐºÑÐ¸Ð¸ Ð¿Ð¾ÑÐµÑÑ Ð¾Ñ Ð½Ð¾Ð¼ÐµÑÐ° ÑÐ¿Ð¾ÑÐ¸. ÐÐ»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÐ°Ð±Ð¾ÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ð±ÑÐ»Ð° Ð¿Ð¾Ð»ÑÑÐµÐ½Ð° ÐµÐ³Ð¾ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ñ Ð¼Ð°ÑÑÐ¸ÑÑ Ð¾ÑÐ¸Ð±Ð¾Ðº Ð¿ÑÐ¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¾ÑÐ½Ð¾ÑÐµÐ½Ð¸ÑÑ ÑÐ¸Ð³Ð½Ð°Ð»/ÑÑÐ¼ Ð² Ð´Ð¸Ð°Ð¿Ð°Ð·Ð¾Ð½Ðµ Ð¾Ñ -20 Ð´Ð Ð´Ð¾ +20 Ð´Ð. ÐÑÐ»Ð¾ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ð¾Ñ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ñ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ ÑÐ»Ð¾ÑÐ² Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. Ð¡Ð´ÐµÐ»Ð°Ð½Ñ ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÑÑÑÐ¸Ðµ Ð²ÑÐ²Ð¾Ð´Ñ Ð¾ ÐºÐ°ÑÐµÑÑÐ²Ðµ ÑÐ°Ð±Ð¾ÑÑ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ°. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð½Ð°Ð¸Ð»ÑÑÑÐ°Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° ÑÐ°Ð²Ð½ÑÐµÑÑÑ 94%.
description: This work is devoted to the use of convolutional neural networks for the implementation of a signal classifier with analog and digital modulation. The review presents modern methods of automatic classification of signal modules. The advantages and disadvantages of the considered methods and algorithms are noted. The structures of the models of the classifier based on deep learning are described. A convolutional neural network is used as the decisive model of the classifier. A simulation model is presented. The objective function and the optimal training parameters for the implementation of the classifier are selected. The quality of training the classifier using the dependence of the loss function on the epoch number is considered. To assess the work of the classifier, its accuracy was obtained and confusion matrix were constructed for various signal-to-noise ratios in the range from -20 dB to +20 dB. A study was carried out of the accuracy of the classifier from the increase in the number of convolutional layers of the neural network. The corresponding conclusions about the work of the implemented classifier are made. As a result, the best classifier accuracy is 94%.