title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° ÑÐ¸Ð¿Ð¾Ð² Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¹ Ð¾Ð´Ð½Ð¾ÑÐ°ÑÑÐ½ÑÑ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð² Ð¼Ð½Ð¾Ð³Ð¾Ð»ÑÑÐµÐ²Ð¾Ð¼ ÐºÐ°Ð½Ð°Ð»Ðµ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development of a modulation classifier in single-frequency signals in a multi-path channel based on neural networks
Creators: name: ÐÐ±Ñ ÐÐ¾Ð½Ð·ÐµÑ, ÐÐ¾Ð°Ð¼Ð¼Ð°Ñ
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: 2022
Subjects: ÐÐ¾Ð´ÑÐ»ÑÑÐ¾ÑÑ
Ð¡Ð¸Ð³Ð½Ð°Ð»Ñ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¸
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¾ÑÑÐ°ÑÐ¾ÑÐ½ÑÐµ ÑÐµÑÐ¸
Ð¼Ð½Ð¾Ð³Ð¾Ð»ÑÑÐµÐ²ÑÐµ Ð·Ð°Ð¼Ð¸ÑÐ°ÑÑÐ¸Ðµ ÐºÐ°Ð½Ð°Ð»Ñ
modulation classification
deep learning
residual networks
multipath fading channels

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description: The topic of the graduate qualification work: "Development of a modulation classifier in single-frequency signals in a multipath channel based on neural networks". This work is devoted to the application of residual convolutional deep neural networks to implement a classifier of Â analogy and digital modulation in single-frequency multipath fading channels. A comparison of existing classical and modern classification methods is performed. The advantages and disadvantages of the considered approaches are listed. Using MATLAB, a new dataset containing many radio signals is generated. The influence of multipath fading channels on these signals is modelled and added. The effect of the clock difference between transmitter and receiver has been added also, and the corresponding time and frequency shift has been added to the generated signals. A deep convolutional neural network based on residual and skipping connections is used as the classifier model. The stochastic gradient descent based on the softmax loss function was chosen as the optimization function. Data normalization and scaling were applied, and finally hyper-parameters were tuned to provide the best performance. The accuracy of the classifier was evaluated at various signal-to-noise ratios in the range [-20,28] db. These test signals simulate the case of AWGN channels and fading multipath channels with EPA, EVA, and ETU parameters. The performance of the classifier for each modulation type at each channel model is also shown separately. A comparison of the performance of the proposed model and the modern model is also presented.