title: ÐÐ½Ð°Ð»Ð¸Ð· Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐµÐ¹ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ 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: Analysis of the specific ap-plication of machine learning techniques for classification signals with analog and digital modulation
Creators: name: ÐÑÐ½ Ð¤ÑÐ»Ð¸Ð½Ñ
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: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¹
Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼
ÐºÐ¾Ð¼Ð±Ð¸Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð²
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð²
Ð°Ð½Ð°Ð»Ð¾Ð³Ð¾Ð²Ð°Ñ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ñ
ÑÐ¸ÑÑÐ¾Ð²Ð°Ñ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ñ
machine learning
modulation classification
genetic algorithm
feature combination
signal recognition
analogue modulation
digital modulation

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð°Ð½Ð°Ð»Ð¸Ð·Ñ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐµÐ¹ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ 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. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¾Ð²ÐµÐ´ÑÐ½ Ð¾Ð±Ð·Ð¾Ñ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¹, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð», ÑÑÐ¾ ÑÐ¿ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÐµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÐºÐ°Ðº Ð°Ð½Ð°Ð»Ð¾Ð³Ð¾Ð²Ð¾Ð¹, ÑÐ°Ðº Ð¸ ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð¹ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¸. Ð¡ÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸ Ð²ÑÑÐ¾ÐºÐ¾Ð³Ð¾ Ð¿Ð¾ÑÑÐ´ÐºÐ°, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑÑÑ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ¸ÑÑÐ¾Ð²ÑÑ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¹. Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð¾ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð²ÑÐ¿Ð¾Ð»Ð½ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¹, Ð¸ Ð¾Ð¿Ð¸ÑÐ°Ð½Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ð´Ð»Ñ ÑÐ°ÑÑÐ¸ÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð². ÐÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÑ KNN Ð¸ SVM - ÑÑÐ¾ Ð´Ð²Ð° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ ÑÑÐ¸ÑÐµÐ»ÐµÐ¼, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÑÐ¸ÑÐ¾Ð²Ð°ÑÑ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¸Ñ Ð²ÑÐ¾Ð´Ð½ÑÑ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð². ÐÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾Ñ KNN Ð»ÐµÐ³ÑÐµ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°ÑÑ Ð´Ð»Ñ Ð·Ð°Ð´Ð°Ñ Ñ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¸Ð¼Ð¸ ÐºÐ»Ð°ÑÑÐ°Ð¼Ð¸, Ð² ÑÐ¾ Ð²ÑÐµÐ¼Ñ ÐºÐ°Ðº ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾Ñ SVM Ð¾Ð±ÐµÑÐ¿ÐµÑÐ¸Ð²Ð°ÐµÑ ÑÐ°Ð·Ð´ÐµÐ»ÑÑÑÑÑ Ð³Ð¸Ð¿ÐµÑÐ¿Ð»Ð¾ÑÐºÐ¾ÑÑÑ, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð° Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿Ð¾ Ð´Ð²ÑÐ¼ ÐºÐ»Ð°ÑÑÐ°Ð¼. ÐÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð²ÑÐ±Ð¸ÑÐ°ÑÑ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸ Ð¸ ÑÐ¾Ð·Ð´Ð°Ð²Ð°ÑÑ ÐºÐ¾Ð¼Ð±Ð¸Ð½Ð°ÑÐ¸Ð¸ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð². ÐÐ½Ð°Ð»Ð¸Ð· ÑÐ°Ð±Ð¾Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð», ÑÑÐ¾ Ð½Ð° ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ð²Ð»Ð¸ÑÑÑ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ¸ ÐºÐ°Ð½Ð°Ð»Ð° ÑÐ²ÑÐ·Ð¸ Ð¸ ÑÐ°Ð·Ð¾Ð²ÑÐ¹ ÑÐ´Ð²Ð¸Ð³. ÐÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÐºÐ¾Ð¼Ð±Ð¸Ð½Ð°ÑÐ¸Ð¸ GP Ð¸ KNN Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÐµÑ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð»ÑÑÑÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸, Ð¾Ð´Ð½Ð°ÐºÐ¾ Ð¸Ð¼ÐµÐµÑ Ð±Ð¾Ð»ÐµÐµ Ð²ÑÑÐ¾ÐºÑÑ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÑ ÑÐ»Ð¾Ð¶Ð½Ð¾ÑÑÑ, ÑÐµÐ¼ Ð±Ð°Ð·Ð¾Ð²ÑÐ¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾Ñ KNN, Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑÐ¸Ð¹ ÑÐµ Ð¶Ðµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸.
description: The subject of the graduate qualification work is Â«Analysis of the specific application of machine learning techniques for classification signals with analog and digital modulationÂ». The given work is devoted to the analysis of the features of the application of machine learning methods for the classification of signals with analog and digital modulation. The paper reviews the existing features for the classification of modulations, which showed that spectral features are suitable for the classification of both analog and digital modulation. High-order statistical features are used to classify digital modulations. The paper considers several machine learning algorithms that perform the classification of modulations, and describes approaches for expanding features. KNN and SVM classifiers are two supervised learning algorithms that allow you to classify modulation using multiple input features. The KNN classifier is easier to implement for multi-class problems, while the SVM classifier provides a dividing hyperplane that is optimized for classification into two classes. Genetic programming allows you to select traits and create combinations of traits. The analysis of the works showed that the characteristics of the communication channel and the phase shift affect the accuracy of the classifier. A classifier based on a combination of GP and KNN shows the best classification accuracy, however, it has a higher computational complexity than the basic KNN classifier using the same features.