title: ÐÐ¾Ð²ÑÑÐµÐ½Ð¸Ðµ Ð¿Ð¾Ð¼ÐµÑ Ð¾ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾ÑÑÐ¸ ÑÐ¿ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾-ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÑ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð¿ÑÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
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: Improving the BER performance of spectrally effective signals using machine learning methods
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: 2023
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¡Ð¸Ð³Ð½Ð°Ð»Ñ
Ð Ð°Ð´Ð¸Ð¾ÑÐ¸Ð³Ð½Ð°Ð»Ñ
Ð Ð°Ð´Ð¸Ð¾Ð¿Ð¾Ð¼ÐµÑ Ð¸
Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ°Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð´ÐµÐ¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ñ
ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÐµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸
automatic classification
demodulation
convolutional features

description: Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸Ð¹ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾Ñ ÑÐ¸Ð¿Ð¾Ð² Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¸ BPSK Ð¸ QPSK Ð´Ð»Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² SEFDM, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¼ÐµÑÐ¾Ð´Ñ Ð´ÐµÐ¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¸ Ð´Ð»Ñ Ð½Ð¸Ñ. ÐÐ»Ð³Ð¾ÑÐ¸ÑÐ¼ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¾ÑÐ½Ð¾Ð²Ð°Ð½ Ð½Ð° Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ð¸ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹, Ð° Ð²ÑÐ¾Ð´Ð½ÑÐ¼Ð¸ Ð´Ð°Ð½Ð½ÑÐ¼Ð¸ Ð´Ð»Ñ Ð½ÐµÐ³Ð¾ ÑÐ»ÑÐ¶Ð°Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ ÑÐ¸Ð³Ð½Ð°Ð»ÑÐ½ÑÑ ÑÐ¾Ð·Ð²ÐµÐ·Ð´Ð¸Ð¹. Ð¢Ð¾ÑÐ½Ð¾ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð´Ð»Ñ Ð¾Ð±Ð¾Ð¸Ñ ÑÐ¸Ð¿Ð¾Ð² Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¸ Ð¿ÑÐ¸Ð½Ð¸Ð¼Ð°ÐµÑ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ Ð²ÑÑÐµ 99% Ð¿ÑÐ¸ Eb/N0 = 7 Ð´Ð Ð¸ Ð±Ð¾Ð»ÐµÐµ. ÐÑÐ¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð´ÐµÐ¼Ð¾Ð´ÑÐ»ÑÑÐ¾ÑÐ¾Ð² Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð»Ð¸ÑÑ Ð´Ð²Ð° Ð¿ÑÐ¸Ð½ÑÐ¸Ð¿Ð° Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ: ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¸ ÑÐµÐ³ÑÐµÑÑÐ¸Ñ. ÐÐ° Ð¿ÑÐ¸Ð¼ÐµÑÐµ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² OFDM-QPSK Ð¿Ð¾ÐºÐ°Ð·Ð°Ð½Ð° Ð½ÐµÑÐ¾ÑÑÐ¾ÑÑÐµÐ»ÑÐ½Ð¾ÑÑÑ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸, ÑÐ°Ðº ÐºÐ°Ðº Ð¿ÑÐ¸ ÐµÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð½Ð°Ð±Ð»ÑÐ´Ð°ÐµÑÑÑ Ð¿ÑÐ¾Ð¸Ð³ÑÑÑ Ð¾Ñ 1,3 Ð´Ð Ð¸ Ð²ÑÑÐµ Ð¾ÑÐ½Ð¾ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾ Ð¿Ð¾ÑÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾Ð¹ Ð¿Ð¾Ð¼ÐµÑÐ¾ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾ÑÑÐ¸. ÐÑÐ¸ Ð´ÐµÐ¼Ð¾Ð´ÑÐ»ÑÑÐ¸Ð¸ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² SEFDM-BPSK Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½ÑÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÑÐµÑ Ð²ÑÐ¸Ð³ÑÑÑ Ð¾ÑÐ½Ð¾ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾ Ð¿ÑÐ¸ÑÐ¼Ð° Ñ Ð¾Ð±ÑÐ°ÑÐ½Ð¾Ð¹ ÑÐ²ÑÐ·ÑÑ Ð´Ð¾ 2,3 Ð´Ð (Ð¿Ð¾ ÑÑÐ¾Ð²Ð½Ñ BER = 10â3). ÐÐ»Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² SEFDM-QPSK ÑÑÐµÐ´Ð½ÑÑ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÑ Ð±Ð¸ÑÐ¾Ð²Ð¾Ð¹ Ð¾ÑÐ¸Ð±ÐºÐ¸ Ð¿ÑÐ¸ Eb/N0 = 15 Ð´Ð ÑÐ¼ÐµÐ½ÑÑÐ¸Ð»Ð°ÑÑ Ð² 7 ÑÐ°Ð·. Ð£ÑÑÐµÐ½Ñ ÑÐµÑÐ¼Ð¸Ð½Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ðµ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐµÐ´Ð¼ÐµÑÐ½Ð¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ñ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÐµ ÑÑÐµÐ´ÑÑÐ²Ð° Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ. ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¾ ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ðµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾-Ð¼Ð°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ðµ MATLAB.
description: As a result of the work, an automatic classifier of BPSK and QPSK modulation types for SEFDM signals was developed based on convolutional neural networks and images of signal constellations. The classification accuracy for both types of modulation takes values above 99% at Eb/N0 = 7 dB or more. Methods for demodulating BPSK and QPSK signals in the SEFDM system have also been developed. Two approaches have been explored: classification and regression. On the example of OFDM-QPSK signals, the regression is shown to be inconsistent, since when using it, a loss of 1.3 dB or more is observed relative to potential BER performance. When demodulating SEFDM-BPSK signals, the proposed algorithm demonstrates a gain in relation to reception with feedback up to 2.3 dB (in terms of BER = 10â3). For SEFDM-QPSK signals, the average bit error probability at Eb/N0 = 15 dB decreased by a factor of 7. The terminological features of the subject area are considered, and software tools are used to solve problems. Specialized software was used: MATLAB.