Implementation of the digital twin of the module for recognizing types of digital modulation of radio signals
- 1,2 JSC “CNIRTI named after academician A.I. Berg” (Moscow, Russia)
Abstract
Problem statement. The recognition of types of radio signal modulation is a significant task that is being solved in radio engineering systems of various applications. The article substantiates the relevance of creating a twin module for recognizing types of digital radio signal modulation.
Goal. Creation creation and implementation of a digital twin module for recognizing types of digital modulation of radio signals based on their statistical characteristics
Results. A digital twin of the digital signal modulation type recognition module based on cumulative analysis has been developed.
Practical significance. The developed algorithm for the dynamic formation of a training sample in the process of real operation eliminates the need for manual training of models when conditions change. The architecture of the adaptive cumulative analyzer makes it possible to optimize the selection of features depending on the noise level, modulation, and other factors affecting reception.
- Received
- 09.04.2025
- Approved after review
- 15.04.2025
- Accepted
- 30.04.2025
How to cite
Курбаналиев В.К., Фесенко М.В. Реализация цифрового двойника модуля распознавания видов цифровой модуляции радиосигналов // Радиотехника. 2025. Т. 89. № 5. С. 105-112. DOI: https://doi.org/10.18127/j00338486-202505-11
References
- Cifrovye dvojniki. Nauch. serija «Prinjatie reshenij v upravlenii». Monografija. Pod red. P.A. Sozinova. M.: Radiotehnika. 2022. 312 s. (in Russian).
- Boschert S., Rosen R. Digital twin - the simulation aspect. In: Mechatronic Futures. Springer. 2016. P. 59–74.
- Cui T., et al. Performance Evaluation of Digital Twin for Cognitive Radio Systems. IEEE Systems Journal. 2022. V. 16(4). P. 5760–5770.
- Rasheed A., San O., Kvamsdal T. Digital twin: values, challenges and enablers. ASME Journal of Computing and Information Science in Engineering. 2020. V. 20(3). Р. 031002.
- Grieves M. Digital twin: manufacturing excellence through virtual factory replication. White Paper. 2014.
- Tao F., Qi Q., Liu A., Kusiak A. Digital twins and cyber – physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering. 2019. V. 5(4). P. 653–661.
- Leonov V.P., Shirjaev A.N. K tehnike vychislenija seminvariantov. Teorija verojatnosti i ee primenenie. 1959. T. 4. Vyp. 3. S. 342–355 (in Russian).
- Kendall M.Dzh., St'juart A. Teorija raspredelenij. M.: Nauka. 1966 (in Russian).
- Malahov A.N. Kumuljantnyj analiz sluchajnyh negaussovyh processov i ih preobrazovanij. M.: Sovetskoe radio. 1978. 376 s. (in Russian).
- Kurbanaliev V.K., Fesenko M.V., Gorbunov Ju.N. Ispol'zovanie kumuljantnogo analiza dlja raspoznavanija cifrovyh vidov moduljacii radiosignalov. Radiotehnika. 2024. T. 88. № 5. S. 39-49. DOI: https://doi.org/10.18127/j00338486-202405-04 (in Russian).
- Adzhemov S.S., Klenov N.V., Tereshonok M.V., Chirov D.S. Metody raspoznavanija vidov cifrovoj moduljacii signalov v kognitivnyh radiosistemah. Vestnik Moskovskogo universiteta. Ser. 3. Fizika. Astronomija. 2015. № 6. S. 19-27 (in Russian).
- Haykin S. Communication Systems. 5th Edition. Wiley. 2009. 608 p.
- Proakis J.G., Salehi M. Digital Communications. 5th Edition. Mc. Graw-Hill. 2007. 1150 p.
- Nguyen H.Q., Kim D. Deep Learning-Based Modulation Classification Using Time–Frequency Features. Sensors. 2021. V. 21(14).
Р. 4847.