title: Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° ÑÐ¸Ð¿Ð¾Ð² Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¹ OFDM-ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð²
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: Implementation of the classifier of modulation types of OFDM signals
Creators: name: ÐÐ¾ÑÐ±ÑÐ½Ð¾Ð², ÐÐ³Ð¾Ñ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: 2021
Subjects: Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ°Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸ ÑÐ¸Ð³Ð½Ð°Ð»Ð°
ÑÐ¸Ð¿Ñ Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¸
OFDM
Ð¸Ð¼Ð¸ÑÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸
automatic classification
neural network
signal features
modulation types
simulation
classification accuracy

description: ÐÐ²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ°Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐ¸Ð¿Ð¾Ð² Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¹ ÑÐ²Ð»ÑÐµÑÑÑ Ð²Ð°Ð¶Ð½ÑÐ¼ ÑÑÐ°Ð¿Ð¾Ð¼ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐ¸ÑÐ¼Ð¾Ð¿ÐµÑÐµÐ´Ð°ÑÑÐ¸ÐºÐ°. ÐÐ´Ð½Ð°ÐºÐ¾ Ð² Ð»Ð¸ÑÐµ-ÑÐ°ÑÑÑÐµ ÑÑÑÐµÑÑÐ²ÑÐµÑ Ð½Ðµ ÑÐ°Ðº Ð¼Ð½Ð¾Ð³Ð¾ ÑÐ°Ð±Ð¾Ñ, Ð¿Ð¾ÑÐ²ÑÑÑÐ½Ð½ÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ¸Ð³-Ð½Ð°Ð»Ð¾Ð² OFDM. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð±ÑÐ» Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐµÑÑÐ¸ ÑÐ¸Ð¿Ð¾Ð² Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¹ OFDM ÑÐ¸Ð³Ð½Ð°Ð»Ð°: BPSK, QPSK, 16,64,256,1024-QAM. ÐÐµÑÐ¾Ð´ Ð¾ÑÐ½Ð¾Ð²Ð°Ð½ Ð½Ð° Ð¸Ð·Ð²Ð»ÐµÑÐµÐ½Ð¸Ð¸ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð², Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ ÐºÐ¾ÑÐ¾ÑÑÑ Ð±ÑÐ»Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÐºÐ¸ Ð¾ÑÐºÐ»Ð¾Ð½ÐµÐ½Ð¸Ñ Ð¼Ð³Ð½Ð¾Ð²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ Ð°Ð¼Ð¿Ð»Ð¸ÑÑÐ´Ñ, ÐºÑÐ¼ÑÐ»ÑÐ½ÑÑ Ð¸ Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð°Ñ Ð²ÐµÐ¹Ð²Ð»ÐµÑ ÑÐ½ÑÑÐ¾Ð¿Ð¸Ñ. Ð¡ ÑÐµÐ»ÑÑ Ð²ÑÑÐ²Ð¸ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼, Ð´Ð°ÑÑÐ¸Ð¹ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÐ¹ ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ Ð¿Ð¾ ÐºÐ°ÑÐµÑÑÐ²Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ Ð¿Ð¾ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ ÑÐ°Ð±Ð¾ÑÑ, Ð±ÑÐ»Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ ÑÐ¾ÑÐµÑÐ°Ð½Ð¸Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµ-Ð¼ÑÑ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð². ÐÐ»Ñ ÐºÐ°Ð¶Ð´Ð¾Ð¹ ÐºÐ¾Ð¼Ð±Ð¸Ð½Ð°ÑÐ¸Ð¸ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð±ÑÐ»Ð¾ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ Ð¸Ð¼Ð¸-ÑÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¿ÑÐ¸ÑÐ¼Ð° OFDM ÑÐ¸Ð³Ð½Ð°Ð»Ð°, Ð¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ñ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸-ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ ÐºÑÐ¸Ð²ÑÐµ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ÑÑÐ»ÐµÐ´ÑÐµÐ¼ÑÑ ÑÐ¸Ð¿Ð¾Ð² Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¹. ÐÐ¾ Ð½Ð¸Ð¼ Ð±ÑÐ»Ð° Ð²ÑÑÐ²Ð»ÐµÐ½Ð° Ð½Ð°Ð¸Ð»ÑÑÑÐ°Ñ ÐºÐ¾Ð¼Ð±Ð¸Ð½Ð°ÑÐ¸Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² â 5 ÐºÑÐ¼ÑÐ»ÑÐ½ÑÐ¾Ð² Ð¸ 7 Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð², Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÑ Ð½Ð° Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ð¹ Ð²ÐµÐ¹Ð²Ð»ÐµÑ ÑÐ½ÑÑÐ¾Ð¿Ð¸Ð¸. ÐÐ»Ñ Ð´Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð½Ð°Ð±Ð¾ÑÐ° Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð´Ð¾Ð¿Ð¾Ð»Ð½Ð¸ÑÐµÐ»ÑÐ½Ð¾ Ð±ÑÐ»Ð¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ñ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ðµ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸-ÑÑÐ¸ÐºÐ¸: ÐºÑÐ¸Ð²ÑÐµ Ð¿Ð¾Ð¼ÐµÑÐ¾ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾ÑÑÐ¸, Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐ¸ Ð»Ð¾Ð¶Ð½Ð¾Ð¹ ÑÑÐµÐ²Ð¾Ð³Ð¸, Ð¼Ð°ÑÑÐ¸ÑÐ° Ð¾ÑÐ¸Ð±Ð¾Ðº. Ð¢Ð°ÐºÐ¶Ðµ Ð»ÑÑÑÐ¸Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾Ñ Ð±ÑÐ» Ð¿ÑÐ¾ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½ Ð½Ð° OFDM ÑÐ¸Ð³-Ð½Ð°Ð»Ð°Ñ Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐ¼ ÑÐ¸ÑÐ»Ð¾Ð¼ Ð¿Ð¾Ð´Ð½ÐµÑÑÑÐ¸Ñ. Ð¢Ð¾ÑÐ½Ð¾ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð² ÑÐ°ÐºÐ¾Ð¼ ÑÐ»ÑÑÐ°Ðµ Ð½Ðµ ÑÑÑÐ´ÑÐ¸Ð»Ð°ÑÑ.
description: Automatic classification of modulation types is an important step in the development of an intelligent transceiver. However, there are not many works in the literature devoted to the classification of OFDM signals. In this paper, an algorithm was proposed for the automatic classification of six types of OFDM signal modulations: BPSK, QPSK, 16,64,256,1024-QAM. The method is based on the extraction of features, which were used as the statistics of the de-viation of the instantaneous value of the amplitude, cumulants and adaptive wavelet entropy. In order to identify the algorithm that gives the optimal result in terms of the quality of classification and the speed of work, various combi-nations of the used features were studied. For each combination of features, simulation modeling of the OFDM signal reception was carried out, and char-acteristic curves of the classification accuracy of the studied types of modula-tions were obtained. Based on them, the best combination of features was iden-tified - 5 cumulants and 7 features based on adaptive wavelet entropy. For this set of features, the following characteristics were additionally constructed: noise immunity curves, false alarm probabilities, and an error matrix. Also, the best classifier was tested on OFDM signals with different numbers of subcarri-ers. In this case, the classification accuracy has not deteriorated.