PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Koncepcja architektur sieci neuronowych na potrzeby klasyfikacji sygnałów radiowych

Identyfikatory
Warianty tytułu
EN
The concept of neural network architectures for classification purposes of radio signals
Języki publikacji
PL
Abstrakty
PL
Problem automatycznej klasyfikacji widma jest coraz ważniejszy w dzisiejszych czasach. Jest to kluczowy problem, który należy rozwiązywać w systemach komunikacji bezprzewodowej wykorzystywanych w zastosowaniach wojskowych jaki i cywilnych. Istnieje wiele metod rozwiązujących powyżej przedstawione zagadnienie, tradycyjne, oparte głównie na statystyce oraz wykorzystujące algorytmy sztucznej inteligencji, które są zakwalifikowane do metod zaawansowanych. W artykule przed- stawiono koncepcje dwóch modeli głębokich sieci neuronowych, które rozwiązują problem automatycznej klasyfikacji modulacji w nowatorski sposób. Zaproponowane struktury same dostosowują przetwarzanie otrzymanego sygnału w celu detekcji rodzaju modulacji. Pierwsza struktura wykorzystuje transformatę Wignera-Vill’a, natomiast drugi model wykorzystuje krótko okresową transformatę Fouriera. Obie transformaty są zaimplementowane w warstwie wejściowej z wagami, które oddziałują na parametry tych transformat.
EN
The problem of automatic spectrum classification is becoming more and more important nowadays. This is a key problem that needs to be solved in wireless communication systems used in military and civilian applications. There are many traditional methods that solve the problem presented above, mainly based on statistics and using artificial intelligence algorithms, which are classified as advanced methods. The article presents the concepts of two models of deep neural networks that solve the problem of automatic classification of modulation in an innovative way. The proposed structures themselves adapt the processing of the received signal in order to detect the modulation type. The first structure uses the Wigner-Vill transform, while the second model uses the short period Fourier transform. Both transforms are implemented in the input layer with weights that affect their execution.
Rocznik
Strony
12--17
Opis fizyczny
Bibliogr. 28 poz., schem., tab.
Twórcy
  • Wydział Elektroniki, Wojskowa Akademia Techniczna
Bibliografia
  • [1] A. Alarabi and O. A. S. Alkishriwo. „Modulation Classification Based on Statistical Features and Artificial Neural Network.” 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA. IEEE, 2021. 748-751.
  • [2] A. K. Abdullah, S. B. Sadkhan and A. A. Abdullah H. A. Hamed. „M-ary Quadrature Ampplitude Modulation Classification Using Skewness and Kurtosis.” 1st Babylon International Conference on Information Technology and Science (BICITS). IEEE, 2021. 109-112.
  • [3] Alaa A. Hefnawy, Mustafa M. Abd Elnaby, Fathi E. Abd El-Samie, Rasha M. Al-Makhlasawy. „Modulation classification in the presence of adjacent channel interference using convolutional neural networks.” International Journal of Communication Systems, 2020: 42-95.
  • [4] Chris Baker, Michail Antoniou, Mohammed Jahangir, George Atkinson, Stephen Harman Holly Dale. „SNR-dependent drone classification using convolutional neural networks.” IET Radar, Sonar & Navigation, January 2022: 22-33.
  • [5] Christian Szegedy, Sergey Ioffe. „Batch normalization: accelerating deep network training by reducing internal covariate shift.” ICML’15: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37. Lille, France: JMLR.org, 2015. 448-456.
  • [6] D. Yang, A. E. Gamal, X. Liu. „Deep neural network architectures for modulation classification.” 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. 915-919.
  • [7] H. Wijanto and F. Y. Suratman H. Kurniansyah. „Automatic Modulation Detection Using Non-Linear Transformation Data Extraction And Neural Network Classification.” 2018 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC). IEEE, 2018. 213-216.
  • [8] Hanan S. Ghanem, Hossam M. Kassem, Maha Elsabrouty, Hesham F. A. Hamed, Fathi E. Abd El-Samie, Gerges M. Salama, Rasha M. Al-Makhlasawy. „Deep learning for wireless modulation classification based on discrete wavelet transform.” International Journal of Communication Systems vol 34, 2021: e4980.
  • [9] Hong Fan, Li Li, Bailin Li, Yun Hou. „Adaptive learning cost-sensitive convolutional neural network.” IET Computer Vision vol. 15, August 2021: 346-355.
  • [10] K. Kim and Y. Shin, J. H. Lee. „Feature Image-Based Automatic Modulation Classification Method Using CNN Algorithm.” 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2019. 1-4.
  • [11] K. Wang, H. Moustafa, S. Wang and K. Zhang, Y. Zhang. „Guest Editorial: Blockchain and AI for Beyond 5G Networks.” IEEE Network vol. 34, no. 6 (IEEE Network), November/December 2020: 22-23.
  • [12] L. Bing-bing and Y. Chang-yi, L. Yan-ling. „Modulation classification of MQAM signals using particle swarm optimization and subtractive clustering.” IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS. IEEE, 2010. 1537-1540.
  • [13] L. Cohen. „Time-frequency distributions-a review.” Proceedings of the IEEE, vol. 77, no. 7, July 1989: 941-981.
  • [14] M. Hegarty, W. Haftel, P. A. Sallee, H. Brown Cribbs, H. H. Huang, A. J. Uppal. „High-Performance Deep Learning Classification for Radio Signals.” 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2019. 1026-1029.
  • [15] M. Shalaby, M. A. Aboelazm, H. E. A. Bakr and H. A. A. Mansour, A. Emam. „A Comparative Study between CNN, LSTM, and CLDNN Models in The Context of Radio Modulation Classification.” 2020 12th International Conference on Electrical Engineering (ICEENG). IEEE, 2020. 190-195.
  • [16] M. Sheoran, G. Jajoo and S. K. Yadav, Y. Kumar. „Automatic Modulation Classification Based on Constellation Density Using Deep Learning.” IEEE Communications Letters, vol. 24, no. 6, June 2020: 1275-1278.
  • [17] Qiuxi Jiang, Bing Sun Guotao Wang. „Radar Signal Modulation Recognition Based on Improved Instantaneous Autocorrelation.” 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP). Nanjing, China: IEEE, 2021. 698-702.
  • [18] R. Mukherjee, S. Ghosh, D. Ghosh, S. Ghosh and A. Mukherjee, S. Chatterjee. „Internet of Things and cognitive radio - Issues and challenges.” 2017 4th International Conference on Opto-Electronics and Applied Optics (Optronix). 2017. 1-4.
  • [19] R. Zhou, C. Gravelle. „SDR Demonstration of Signal Classification in Real-Time Using Deep Learning.” 2019 IEEE Globecom Workshops (GC Wkshps). IEEE, 2019. 1-5.
  • [20] Rasha M. Al-Makhlasawy, Nariman Abdel-Salam Bauomy, El-Sayed M. El-Rabaie, Walid El-Shafai, Ahmed E. A. Farghal, Fathi E. Abd El-Samie, Mohamed A. Abdel-Moneim. „An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding.” Transactions on Emerging Telecommunications Technologies, March 2022: 44-59.
  • [21] S. Chen, P. Qi, H. Zhou, X. Yang, S. Zheng. „Spectrum sensing based on deep learning classification for cognitive radios.” China Communications, vol. 17, no. 2, Febreury 2020: 138-148.
  • [22] S. Huang, Y. Zeng, H. Chen, S. Chang and Y. Zhang, J. Huang. „Hierarchical Digital Modulation Classification Using Cascaded Convolutional Neural Network.” Journal of Communications and Information Networks, vol. 6, no. 1, March 2021: 72-81.
  • [23] T. Wan, W. Xiong and J. Liao, H. Ji. „A method for specific emitter identification based on surrounding-line bispectrum and convolutional neural network.” 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2020. 328-332.
  • [24] TACM & Mecklenbräuker, Wolfgang, Claasen. „The Wigner distribution - A tool for time-frequency signal analysis - Part II: Discrete time signals.” Philips J Research, 1980: 276-300.
  • [25] Walid El-Shafai, Nariman Abdel-Salam, El-Sayed M. El-Rabaie, Fathi E. Abd El-Samie Mohamed A. Abdel-Moneim. „A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends.” International Journal of communication systems vol 34, 10 July 2021: 47-62.
  • [26] X. Jing, Y. He, Y. Cui, M. Kadoch and M. Cheriet, Q. Zhou. „LSTM-based Automatic Modulation Classification.” 2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, 2020. 1-4.
  • [27] Yilin Sun, Edward A Ball. „Automatic modulation classification using techniques from image classification.” IET Communications (John Wiley and Sons), January 2022: -.
  • [28] Zhan Xu, Peiyue Zhang, Qianwen Zhang. „Modulation scheme recognition using convolutional neural network.” The Journal of Engineering, 2019: 9075-9078.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-827c6042-b5b7-41db-8acb-8b7050729cdf
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.