title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÑ ÑÑÐµÐ´ÑÑÐ² ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ 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: Development of software for recognizing OFDM signals
Creators: name: ÐÐ¾Ð»ÑÐ°Ð½Ð¾Ð², ÐÐ²Ð°Ð½
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: 2024
Subjects: Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÑ
Ð´ÐµÑÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð²
ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð°
OFDM
neural network
signal detection
spectrogram

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ OFDM ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð² ÑÐ¸ÑÐ¾ÐºÐ¾Ð¿Ð¾Ð»Ð¾ÑÐ½Ð¾Ð¹ ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ðµ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾ Ð±ÑÐ»Ð¾ ÑÐµÑÐ¸ÑÑ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ðµ Ð·Ð°Ð´Ð°ÑÐ¸: - Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð³ÐµÐ½ÐµÑÐ°ÑÐ¾ÑÐ° ÑÐµÑÑÐ¾Ð²ÑÑ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. - ÐÐ±ÑÑÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ YOLOv8. - Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÑ ÑÑÐµÐ´ÑÑÐ² Ð¾ÑÐµÐ½ÐºÐ¸ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¾Ñ ÑÐµÑÑÐ¾Ð²ÑÑ OFDM ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð², ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÑÑÑÐ¸Ñ ÑÑÐ°Ð½Ð´Ð°ÑÑÑ IEEE802.11ac, Ð¸ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ñ QAM, PSK Ð¸ FSK Ð¼Ð¾Ð´ÑÐ»ÑÑÐ¸ÑÐ¼Ð¸, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð·Ð°Ð´Ð°Ð²Ð°ÑÑ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð² ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ðµ, Ð¿Ð¾Ð»Ð¾ÑÑ ÑÐ°ÑÑÐ¾Ñ (20, 40 Ð¸Ð»Ð¸ 80 ÐÐÑ) Ð¸ Ð´Ð»Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ (Ð² Ð´Ð¸Ð°Ð¿Ð°Ð·Ð¾Ð½Ðµ Ð¾Ñ 2,2 Ð´Ð¾ 4,3 Ð¼Ñ) ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð², ÑÐ¼ÐµÑÐµÐ½Ð¸Ðµ Ð¿Ð¾ ÑÐ°ÑÑÐ¾ÑÐµ Ð¸ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð´Ð»Ñ ÐºÐ°Ð¶Ð´Ð¾Ð³Ð¾ ÑÐ¸Ð³Ð½Ð°Ð»Ð°, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¾ÑÐ½Ð¾ÑÐµÐ½Ð¸Ðµ ÑÐ¸Ð³Ð½Ð°Ð»/ÑÑÐ¼. ÐÐµÐ½ÐµÑÐ°ÑÐ¾Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð² ÑÑÐµÐ´Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Matlab Ð¸ Ð¾Ð±ÐµÑÐ¿ÐµÑÐ¸Ð²Ð°ÐµÑ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ðµ ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼ Ñ ÑÐ¸ÑÐ¸Ð½Ð¾Ð¹ Ð¿Ð¾Ð»Ð¾ÑÑ ÑÐ°ÑÑÐ¾Ñ 640 ÐÐÑ. ÐÐ»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð² ÑÐ¸ÑÐ¾ÐºÐ¾Ð¿Ð¾Ð»Ð¾ÑÐ½Ð¾Ð¹ ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ðµ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð½ÑÑ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ YOLOv8n. ÐÐ»Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÑ ÑÑÐµÐ´ÑÑÐ²Ð° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½ ÑÐ·ÑÐº Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python Ð¸ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ° ultralytics. ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÑ ÑÑÐµÐ´ÑÑÐ² Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð² ÑÐ¸ÑÐ¾ÐºÐ¾Ð¿Ð¾Ð»Ð¾ÑÐ½Ð¾Ð¹ ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ðµ Ð² Ð´Ð¸Ð°Ð¿Ð°Ð·Ð¾Ð½Ðµ Ð¾ÑÐ½Ð¾ÑÐµÐ½Ð¸Ñ ÑÐ¸Ð³Ð½Ð°Ð»/ÑÑÐ¼ Ð¾Ñ -20 Ð´Ð Ð´Ð¾ 20 Ð´Ð Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¾, ÑÑÐ¾ Ð´Ð¾Ð»Ñ Ð²ÐµÑÐ½Ð¾ Ð½Ð°Ð¹Ð´ÐµÐ½Ð½ÑÑ Ð¸ Ð²ÐµÑÐ½Ð¾ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð²ÑÑÐµ 0.85 Ð¿ÑÐ¸ Ð¾ÑÐ½Ð¾ÑÐµÐ½Ð¸Ð¸ ÑÐ¸Ð³Ð½Ð°Ð»-ÑÑÐ¼ Ð±Ð¾Ð»ÑÑÐµ -3 Ð´Ð.
description: The topic of the final qualifying work: "Development of software for recognizing OFDM signals" This work is devoted to the development of software for recognizing OFDM signals in a broadband spectrogram using a neural network. During the development, it was necessary to solve the following tasks: â¢ Development of a test signal generator for neural network training. â¢ YOLOv8 neural network training. â¢ Development of software tools for evaluating the results of detection and classification. A generator of test OFDM signals conforming to the IEEE802.11ac standard and signals with QAM, PSK and FSK modulations has been developed, which allows you to set the number of signals in the spectrogram, the frequency band (20, 40 or 80 MHz) and the duration (in the range from 2.2 to 4.3 ms) of the signals, the frequency and time offset for each the signal, as well as the signal-to-noise ratio. The generator is implemented in the Matlab simulation environment and provides the creation of spectrograms with a bandwidth of 640 MHz. The YOLOv8n neural network was used to solve the problems of detecting signals in a broadband spectrogram and classifying the detected signals. The Python programming language and the ultralytics library were used to develop the software. A study of the developed software tools for detecting and classifying signals in a broadband spectrogram in the signal-to-noise ratio range from -20 dB to 20 dB showed that the proportion of correctly found and correctly classified signals is higher than 0.85 with a signal-to-noise ratio greater than -3 dB.