{"id":15879,"date":"2023-02-11T22:31:32","date_gmt":"2023-02-11T20:31:32","guid":{"rendered":"http:\/\/journals.khnu.km.ua\/vestnik\/?p=15879"},"modified":"2023-03-24T13:42:50","modified_gmt":"2023-03-24T11:42:50","slug":"metod-vyznachennya-parametriv-modulyacziyi-sygnaliv-bezpilotnyh-litalnyh-aparativ-z-vykorystannyam-shtuchnyh-nejronnyh-merezh","status":"publish","type":"post","link":"https:\/\/journals.khnu.km.ua\/vestnik\/?p=15879","title":{"rendered":"\u041c\u0435\u0442\u043e\u0434 \u0432\u0438\u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0456\u0432 \u043c\u043e\u0434\u0443\u043b\u044f\u0446\u0456\u0457 \u0441\u0438\u0433\u043d\u0430\u043b\u0456\u0432 \u0431\u0435\u0437\u043f\u0456\u043b\u043e\u0442\u043d\u0438\u0445 \u043b\u0456\u0442\u0430\u043b\u044c\u043d\u0438\u0445 \u0430\u043f\u0430\u0440\u0430\u0442\u0456\u0432 \u0437 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f\u043c \u0448\u0442\u0443\u0447\u043d\u0438\u0445 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0438\u0445 \u043c\u0435\u0440\u0435\u0436"},"content":{"rendered":"

<\/p>\n

\u041c\u0415\u0422\u041e\u0414 \u0412\u0418\u0417\u041d\u0410\u0427\u0415\u041d\u041d\u042f \u041f\u0410\u0420\u0410\u041c\u0415\u0422\u0420\u0406\u0412 \u041c\u041e\u0414\u0423\u041b\u042f\u0426\u0406\u0407 \u0421\u0418\u0413\u041d\u0410\u041b\u0406\u0412 \u0411\u0415\u0417\u041f\u0406\u041b\u041e\u0422\u041d\u0418\u0425 \u041b\u0406\u0422\u0410\u041b\u042c\u041d\u0418\u0425 \u0410\u041f\u0410\u0420\u0410\u0422\u0406\u0412 \u0417 \u0412\u0418\u041a\u041e\u0420\u0418\u0421\u0422\u0410\u041d\u041d\u042f\u041c \u0428\u0422\u0423\u0427\u041d\u0418\u0425 \u041d\u0415\u0419\u0420\u041e\u041d\u041d\u0418\u0425 \u041c\u0415\u0420\u0415\u0416<\/p>\n

METHOD OF DETERMINING PARAMETERS OF MODULATION OF UAV SIGNALS USING ARTIFICIAL NEURAL NETWORKS<\/p>\n

\u0421\u0442\u043e\u0440\u0456\u043d\u043a\u0438: 47-54. \u041d\u043e\u043c\u0435\u0440: \u2116<\/strong>6<\/strong>.\u0442.2, 2022 (315)\u00a0\u00a0<\/strong> \"\"<\/a>
\nDOI:<\/strong> https:\/\/www.doi.org\/10.31891\/2307-5732-2022-315-6(2)-47-54<\/a>
\n\u0410<\/strong>\u0432\u0442\u043e\u0440\u0438<\/strong>: <\/strong>
\n\u0406\u0433\u043e\u0440 \u0413\u0423\u041b\u0410
\n\u0425\u043c\u0435\u043b\u044c\u043d\u0438\u0446\u044c\u043a\u0438\u0439 \u043d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442
\nhttps:\/\/orcid.org\/0000-0002-4434-5794<\/a>
\ne-mail: holmenetwork@gmail.com
\n\u041e\u043b\u0435\u043a\u0441\u0456\u0439 \u041f\u041e\u041b\u0406\u041a\u0410\u0420\u041e\u0412\u0421\u042c\u041a\u0418\u0425
\n\u041e\u0434\u0435\u0441\u044c\u043a\u0438\u0439 \u043d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u043c\u043e\u0440\u0441\u044c\u043a\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442
\nhttps:\/\/orcid.org\/0000-0002-1893-7390<\/a>
\ne-mail: polalexey@gmail.com
\nIhor HULA
\nKhmelnitsky National University
\nOleksiy POLIKAROVSKYKH
\nOdessa National Maritime University<\/p>\n

\u0410\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u043c\u043e\u0432\u043e\u044e \u043e\u0440\u0438\u0433\u0456\u043d\u0430\u043b\u0443<\/strong><\/p>\n

\u0421\u0442\u0430\u0442\u0442\u044f \u043f\u0440\u0438\u0441\u0432\u044f\u0447\u0435\u043d\u0430 \u0440\u043e\u0437\u0433\u043b\u044f\u0434\u0443 \u043f\u0438\u0442\u0430\u043d\u043d\u044f \u0432\u0438\u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0456\u0432 \u043c\u043e\u0434\u0443\u043b\u044f\u0446\u0456\u0457 \u0441\u0438\u0433\u043d\u0430\u043b\u0456\u0432 \u0431\u0435\u0437\u043f\u0456\u043b\u043e\u0442\u043d\u0438\u0445 \u043b\u0456\u0442\u0430\u043b\u044c\u043d\u0438\u0445 \u0430\u043f\u0430\u0440\u0430\u0442\u0456\u0432 \u0437 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f\u043c \u0448\u0442\u0443\u0447\u043d\u0438\u0445 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0438\u0445 \u043c\u0435\u0440\u0435\u0436 \u0448\u043b\u044f\u0445\u043e\u043c \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u043d\u043d\u044f \u0432\u0438\u0434\u0456\u0432 \u0446\u0438\u0444\u0440\u043e\u0432\u043e\u0457 \u043c\u043e\u0434\u0443\u043b\u044f\u0446\u0456\u0457 \u0442\u0430 \u0432\u0438\u043a\u043e\u043d\u0443\u0454\u0442\u044c\u0441\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u043e\u044e, \u044f\u043a\u0430 \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0447\u043d\u043e \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0443\u0454 \u0432\u0438\u0434 \u0446\u0438\u0444\u0440\u043e\u0432\u043e\u0457 \u043c\u043e\u0434\u0443\u043b\u044f\u0446\u0456\u0457 \u043e\u0434\u0435\u0440\u0436\u0443\u0432\u0430\u043d\u043e\u0433\u043e \u0441\u0438\u0433\u043d\u0430\u043b\u0443. \u0420\u043e\u0437\u0440\u043e\u0431\u043b\u0435\u043d\u0438\u0439 \u043c\u0435\u0442\u043e\u0434 \u0432\u0438\u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0456\u0432 \u043c\u043e\u0434\u0443\u043b\u044f\u0446\u0456\u0457 \u0441\u0438\u0433\u043d\u0430\u043b\u0456\u0432, \u0432\u0456\u0434\u0440\u0456\u0437\u043d\u044f\u0454\u0442\u044c\u0441\u044f \u0432\u0456\u0434 \u043d\u0438\u043d\u0456 \u0456\u0441\u043d\u0443\u044e\u0447\u0438\u0445 \u0441\u043f\u043e\u0441\u043e\u0431\u043e\u043c \u0432\u0438\u0431\u043e\u0440\u0443 \u043e\u0437\u043d\u0430\u043a \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0446\u0456\u0457 \u0441\u0438\u0433\u043d\u0430\u043b\u0456\u0432. \u0423 \u0440\u043e\u0431\u043e\u0442\u0456 \u0443 \u044f\u043a\u043e\u0441\u0442\u0456 \u0442\u0430\u043a\u0438\u0445 \u043e\u0437\u043d\u0430\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u0456 \u043a\u0443\u043c\u0443\u043b\u044f\u043d\u0442\u0438 \u044f\u043a \u043f\u0430\u0440\u043d\u043e\u0433\u043e, \u0442\u0430\u043a \u0456 \u043d\u0435\u043f\u0430\u0440\u043d\u043e\u0433\u043e \u043f\u043e\u0440\u044f\u0434\u043a\u0443.
\n\u00a0\u041a\u043b\u044e\u0447\u043e\u0432\u0456 \u0441\u043b\u043e\u0432\u0430:<\/strong> \u0440\u043e\u0437\u043f\u0456\u0437\u043d\u0430\u0432\u0430\u043d\u043d\u044f \u0446\u0438\u0444\u0440\u043e\u0432\u043e\u0457 \u043c\u043e\u0434\u0443\u043b\u044f\u0446\u0456\u0457, \u0431\u0430\u0433\u0430\u0442\u043e\u0448\u0430\u0440\u043e\u0432\u0430 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0430 \u043c\u0435\u0440\u0435\u0436\u0430, \u0441\u0438\u0441\u0442\u0435\u043c\u0430 SDR, \u0411\u041f\u041b\u0410.<\/p>\n

\u0420\u043e\u0437\u0448\u0438\u0440\u0435\u043d\u0430 \u0430\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u0430\u043d\u0433\u043b\u0456\u0439\u0441\u044c\u043a\u043e\u044e \u00a0\u043c\u043e\u0432\u043e\u044e<\/strong><\/p>\n

The article is devoted to the consideration of the issue of determining the parameters of the modulation of signals of unmanned aerial vehicles using artificial neural networks by recognizing the types of digital modulation and is performed by a system that automatically classifies the type of digital modulation of the received signal. Recognition of digital modulation types is used, which automatically classifies the type of digital modulation of the received signal. The following issues are covered in the article: the analysis of existing approaches in the task of automatic recognition of types of digital modulation is carried out; the analysis and classification of informative features in the task of automatic recognition is carried out the following types of digital modulation: 2-PSK, 4-PSK, 8-PSK, 2-FSK, 8-QAM, 16-QAM, 64-QAM and OFDM.
\nThis article uses a neural network approach based on cumulative characteristics. To solve these problems, the methods of calculation and selection of informative cumulative features described in the developed model of the system of automatic recognition of digital modulation types by means of a neural network on cumulative features at a known value of carrier frequency are described.
\nA model of the system of automatic recognition of digital modulation types at a known value is constructed carrier frequency. An algorithm for automatic recognition of digital modulation types has been developed using a multilayer neural network.
\nThe influence of noise in the communication channel on the probability of recognizing the types of digital modulation at a known value of the carrier frequency is investigated. It was found that regardless of the type of noise in the communication channel, the law of error distribution in IQ data becomes close to normal. This fact is one important argument for the use of cumulative features in the task of automatic recognition of types of digital modulation. Therefore, the task of automatic recognition of digital modulation types is quite relevant. Further research may be aimed at expanding the range of high-order cumulative features used, due to which it is possible to increase the probability of correct recognition of types of digital modulation, and solving the recognition problem at an unknown value of the frequency and initial phase of the carrier signal.
\nKeywords:<\/strong> digital modulation recognition, multilayer neural network, SDR system, UAV.<\/p>\n

\u041b\u0456\u0442\u0435\u0440\u0430\u0442\u0443\u0440\u0430<\/strong><\/p>\n

\u00a0<\/strong>\u0410\u0432\u0435\u0434\u044c\u044f\u043d, \u042d.\u0414. \u041a \u0432\u044b\u0431\u043e\u0440\u0443 \u043a\u0443\u043c\u0443\u043b\u044f\u043d\u0442\u043d\u0438\u0445 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 \u0432 \u0437\u0430\u0434\u0430\u0447\u0435 \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044f \u0432\u0438\u0434\u043e\u0432 \u0446\u0438\u0444\u0440\u043e\u0432\u043e\u0439 \u043c\u043e\u0434\u0443\u043b\u044f\u0446\u0456\u0457 \u0440\u0430\u0434\u0438\u043e\u0441\u0438\u0433\u043d\u0430\u043b\u043e\u0432 \/ \u042d.\u0414. \u0410\u0432\u0435\u0434\u044c\u044f\u043d, \u0412.\u041d. \u0414\u0430\u043c \/\/ \u0418\u043d\u0444\u043e\u0440\u043c\u0430\u0442\u0438\u0437\u0430\u0446\u0438\u044f \u0438 \u0441\u0432\u044f\u0437\u044c. – 2015. – \u2116 4. – \u0421. 11 – 15.<\/span><\/p>\n

    \n
  1. \u0410\u0434\u0436\u0435\u043c\u043e\u0432, \u0421.\u0421. \u041c\u0435\u0442\u043e\u0434\u044b \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044f \u0432\u0438\u0434\u043e\u0432 \u0446\u0438\u0444\u0440\u043e\u0432\u043e\u0439 \u043c\u043e\u0434\u0443\u043b\u044f\u0446\u0456\u0457 \u0441\u0438\u0433\u043d\u0430\u043b\u043e\u0432 \u0432 \u043a\u043e\u0433\u043d\u0438\u0442\u0438\u0432\u043d\u044b\u0445 \u0440\u0430\u0434\u0438\u043e\u0441\u0438\u0441\u0442\u0435\u043c\u0430\u0445 \/ \u0421.\u0421. \u0410\u0434\u0436\u0435\u043c\u043e\u0432, \u041d.\u0412. \u041a\u043b\u0435\u043d\u043e\u0432, \u041c.\u0412. \u0422\u0435\u0440\u0435\u0448\u043e\u043d\u043e\u043a, \u0414.\u0421. \u0427\u0438\u0440\u043e\u0432 \/\/ \u0412\u0435\u0441\u0442\u043d\u0438\u043a \u041c\u043e\u0441\u043a\u043e\u0432\u0441\u043a\u043e\u0433\u043e \u0423\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u0430. \u0421\u0435\u0440\u0438\u044f 3. \u0424\u0438\u0437\u0438\u043a\u0430. \u0410\u0441\u0442\u0440\u043e\u043d\u043e\u043c\u0438\u044f. – 2015. – \u2116 6. – \u0421. 19 – 27.<\/li>\n
  2. \u0411\u0430\u043a\u0443\u043b\u0438\u043d, \u041c.\u0413. \u0422\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u044f OFDM: \u0423\u0447\u0435\u0431\u043d\u043e\u0435 \u043f\u043e\u0441\u043e\u0431\u0438\u0435 \u0434\u043b\u044f \u0432\u0443\u0437\u043e\u0432 \/ \u041c.\u0413. \u0411\u0430\u043a\u0443\u043b\u0438\u043d, B.\u0412. \u041a\u0440\u0435\u0439\u043d\u0434\u0435\u043b\u0438\u043d, \u0410.\u041c. \u0428\u043b\u043e\u043c\u0430, \u0410.\u041f \u0428\u0443\u043c\u043e\u0432. – \u041c.: \u0413\u043e\u0440\u044f\u0447\u0430\u044f \u043b\u0438\u043d\u0438\u044f – \u0422\u0435\u043b\u0435\u043a\u043e\u043c, 2017. – 352 \u0441.<\/li>\n
  3. \u0413\u0443\u043b\u0430 \u0406.\u0412. \u0414\u043e\u0441\u043b\u0456\u0434\u0436\u0435\u043d\u043d\u044f \u043d\u0435\u0439\u0440\u043e\u043c\u0435\u0440\u0435\u0436 \u0434\u043b\u044f \u043a\u0435\u0440\u0443\u0432\u0430\u043d\u043d\u044f \u0441\u0438\u0441\u0442\u0435\u043c\u043e\u044e Software Defined Radio \/\u0413\u0443\u043b\u0430 \u0406.\u0412., \u041f\u043e\u043b\u0456\u043a\u0430\u0440\u043e\u0432\u0441\u044c\u043a\u0438\u0445 \u041e.\u0406.\/\/ Herald of Khmelnitskyi national university. \u2013 2021. \u2013 \u21166. \u2013 \u0421. 150-156<\/li>\n
  4. \u0414\u044c\u044f\u043a\u043e\u043d\u043e\u0432, \u0412.\u041f. Scilab \u043f\u043e\u043b\u043d\u044b\u0439 \u0441\u0430\u043c\u043e\u0443\u0447\u0438\u0442\u0435\u043b\u044c \/ \u0412.\u041f. \u0414\u044c\u044f\u043a\u043e\u043d\u043e\u0432. – \u041c.: \u0414\u041c\u041a \u041f\u0440\u0435\u0441\u0441, 2012. – 768 \u0441.<\/li>\n
  5. \u041c\u0430\u0433\u0434\u0430, \u042e.\u0421. LabVIEW: \u043f\u0440\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u043a\u0443\u0440\u0441 \u0434\u043b\u044f \u0438\u043d\u0436\u0435\u043d\u0435\u0440\u043e\u0432 \u0438 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u043e\u0432 \/ \u042e.\u0421. \u041c\u0430\u0433\u0434\u0430. – \u041c.: \u0414\u041c\u041a \u041f\u0440\u0435\u0441\u0441, 2012. – 208 \u0441.<\/li>\n
  6. \u041c\u0430\u043b\u0430\u0445\u043e\u0432, \u0410.\u041d. \u041a\u0443\u043c\u0443\u043b\u044f\u043d\u0442\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437 \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u0445 \u043d\u0435\u0433\u0430\u0443\u0441\u0441\u043e\u0432\u044b\u0445 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0432 \u0438 \u0438\u0445 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0439 \/ \u0410. \u041d \u041c\u0430\u043b\u0430\u0445\u043e\u0432. – \u041c.: \u0421\u043e\u0432\u0435\u0442\u0441\u043a\u043e\u0435 \u0440\u0430\u0434\u0438\u043e, 1978. – 376 c.<\/li>\n
  7. \u041c\u0435\u0434\u0432\u0435\u0434\u0435\u0432, \u0412.\u0421. \u041d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0435 \u0441\u0435\u0442\u0438. Scilab 6 \/ \u0412.\u0421. \u041c\u0435\u0434\u0432\u0435\u0434\u0435\u0432, \u0412.\u0413. \u041f\u043e\u0442\u0435\u043c\u043a\u0438\u043d.- \u041c.: \u0414\u0418\u0410\u041b\u041e\u0413-\u041c\u0418\u0424\u0418, 2001. – 630 \u0441.<\/li>\n
  8. \u0425\u0430\u0439\u043a\u0438\u043d, \u0421. \u041d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0435 \u0441\u0435\u0442\u0438: \u043f\u043e\u043b\u043d\u044b\u0439 \u043a\u0443\u0440\u0441 \/ \u0421. \u0425\u0430\u0439\u043a\u0438\u043d; \u043f\u0435\u0440. \u0441 \u0430\u043d\u0433\u043b. \u043f\u043e\u0434 \u0440\u0435\u0434. \u041d.\u041d. \u041a\u0443\u0441\u0441\u0443\u043b\u044c. – 2-\u0435 \u0438\u0437\u0434. – \u041c.: \u0418\u0437\u0434\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u0434\u043e\u043c \u201c\u0412\u0438\u043b\u044c\u044f\u043c\u0441\u201d, 2006. – 1104 \u0441.<\/li>\n
  9. Marko, M. R. Application of artificial neural networks in classification of digital modulations for software defined radio \/ M.R. Marko, M.N. Aleksandar, J.N. Natasa \/\/ IEEE EUROCON 2009. – P. 1700 – 1706.<\/li>\n
  10. Taira, S. Automatic classification of QAM signals by neural networks \/ S. Taira \/\/ Acoustics, Speech, and Signal Processing (ICASSP ’01), 2001. -Vol. 2. – P. 1309 – 1312.<\/li>\n
  11. Young, A. F. Classification of digital modulation types in multipath environ\u0448ents \/A.F. Young \/\/ Master\u2019s Thesis. Naval Postgraduate Schoo1, Monterey, CA 93943-5000, California. June 2008. P. 1 – 65.<\/li>\n
  12. Velampalli, C. Hierarchical blind modulation classification in the presence of carrier frequency offset \/ C. Velampalli \/\/ Master\u2019s Thesis. Communications Research Center, May 2010. – P. 1 – 39.<\/li>\n
  13. Tejwant, S. Performance comparison of fuzzy c-means with respect to other clustering algjrithm \/ S. Tejwant, M. Mr. Manish \/\/ International journal of advanced research in computer science and software engineering, May 2014. – Vol. 4. – Issue 5. – P. 89 – 93.<\/li>\n
  14. Bagga, J. Automatic modulation classification using statistical features in fading environment\/ J. Bagga, N. Tripathi \/\/ International Journal of Advanced Research in electrical, electronics and instrumentation engineering, August 2013, Vol. 2, Issue 8. P. 3701 – 3709.<\/li>\n
  15. Muhlhaus, M.S. Automatic Modulation Classification for MIMO Systems Using Fourth-Order Cumulants \/ M. S. Muhlhaus, M. Oner, O. A. Dobre, H. U. Jakel, F. K. Jondral \/\/ Vehicular Technology Conference (VTC Fall). 2012 IEEE. P. 1 – 5.<\/li>\n
  16. Marchard, P. Classification of linear modulations by a combination of different orders cyclic cumulants \/ P. Marchard, J. L. Lacoume, C. Martret \/\/ Proc. Workshop on HOS, 1997. P. 47 – 51.<\/li>\n
  17. Narendar, M. Modulation classification for cognitive radios using cumulants based on fractional lower order statistics \/ M. Narendar, A. P. Vinod, A. S. Madhukumar and<\/li>\n
  18. Krishna. \/\/ General Assembly and Scientific Symposium, 2011 XXXth URSI. – P. 1 – 4.<\/li>\n
  19. Dobre, O. Higher-order cyclic cumulants for high order digital modulation classification \/ O. Dobre, Y. BarNess, W. Su \/\/ Proceedings of IEEE MILCOM 2003, October 2003. – Vol. 1. – P. 112 – 117.<\/li>\n
  20. Iversen, A. Classification of digital modulation schemes using multi-layered perceptrons \/ A. Iversen \/\/ Report, School of Mathematical and Computer Sciences, Heriot-Watt University, 2004.<\/li>\n
  21. Wong, M. L. D. Automatic digital modulation recognition using artificial neural network and genetic algorithm \/ M.L.D. Wong, A.K Nandi \/\/ Signal Processing 84. 2004. – P. 351 – 365.<\/li>\n<\/ol>\n

    References<\/strong><\/p>\n

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    7. Malakhov, A.N. Kumuliantn\u044bi analyz sluchain\u044bkh nehaussov\u044bkh protsessov y ykh preobrazovanyi \/ A. N Malakhov. – M.: Sovetskoe radyo, 1978. – 376 c.<\/li>\n
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    10. Marko, M. R. Application of artificial neural networks in classification of digital modulations for software defined radio \/ M.R. Marko, M.N. Aleksandar, J.N. Natasa \/\/ IEEE EUROCON 2009. – P. 1700 – 1706.<\/li>\n
    11. Taira, S. Automatic classification of QAM signals by neural networks \/ S. Taira \/\/ Acoustics, Speech, and Signal Processing (ICASSP 01), 2001. -Vol. 2. – P. 1309 – 1312.<\/li>\n
    12. Young, A. F. Classification of digital modulation types in multipath environshents \/A.F. Young \/\/ Masters Thesis. Naval Postgraduate Schoo1, Monterey, CA 93943-5000, California. June 2008. P. 1 – 65.<\/li>\n
    13. Velampalli, C. Hierarchical blind modulation classification in the presence of carrier frequency offset \/ C. Velampalli \/\/ Masters Thesis. Communications Research Center, May 2010. – P. 1 – 39.<\/li>\n
    14. Tejwant, S. Performance comparison of fuzzy c-means with respect to other clustering algjrithm \/ S. Tejwant, M. Mr. Manish \/\/ International journal of advanced research in computer science and software engineering, May 2014. – Vol. 4. – Issue 5. – P. 89 – 93.<\/li>\n
    15. Bagga, J. Automatic modulation classification using statistical features in fading environment\/ J. Bagga, N. Tripathi \/\/ International Journal of Advanced Research in electrical, electronics and instrumentation engineering, August 2013, Vol. 2, Issue 8. P. 3701 – 3709.<\/li>\n
    16. Muhlhaus, M.S. Automatic Modulation Classification for MIMO Systems Using Fourth-Order Cumulants \/ M. S. Muhlhaus, M. Oner, O. A. Dobre, H. U. Jakel, F. K. Jondral \/\/ Vehicular Technology Conference (VTC Fall). 2012 IEEE. P. 1 – 5.<\/li>\n
    17. Marchard, P. Classification of linear modulations by a combination of different orders cyclic cumulants \/ P. Marchard, J. L. Lacoume, C. Martret \/\/ Proc. Workshop on HOS, 1997. P. 47 – 51.<\/li>\n
    18. Narendar, M. Modulation classification for cognitive radios using cumulants based on fractional lower order statistics \/ M. Narendar, A. P. Vinod, A. S. Madhukumar and<\/li>\n
    19. K. Krishna. \/\/ General Assembly and Scientific Symposium, 2011 XXXth URSI. – P. 1 – 4.<\/li>\n
    20. Dobre, O. Higher-order cyclic cumulants for high order digital modulation classification \/ O. Dobre, Y. BarNess, W. Su \/\/ Proceedings of IEEE MILCOM 2003, October 2003. – Vol. 1. – P. 112 – 117.<\/li>\n
    21. Iversen, A. Classification of digital modulation schemes using multi-layered perceptrons \/ A. Iversen \/\/ Report, School of Mathematical and Computer Sciences, Heriot-Watt University, 2004.<\/li>\n
    22. Wong, M. L. D. Automatic digital modulation recognition using artificial neural network and genetic algorithm \/ M.L.D. Wong, A.K Nandi \/\/ Signal Processing 84. 2004. – P. 351 – 365.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[75],"tags":[],"_links":{"self":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/15879"}],"collection":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=15879"}],"version-history":[{"count":5,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/15879\/revisions"}],"predecessor-version":[{"id":16420,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/15879\/revisions\/16420"}],"wp:attachment":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15879"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15879"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15879"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}