D.V. Ivanov1, V.A. Ivanov2, M.I. Ryabova3, V.V. Ovchinnikov4, N.A. Konkin5

1–5 Volga State University of Technology (Yoshkar-Ola, Russia)

1 IvanovDV@volgatech.net, 2 IvanovVA@volgatech.net, 3 RyabovaMI@volgatech.net, 4 OvchinnikovVV@volgatech.net, 5 KonkinNA@volgatech.net

It is well established that the principal challenges in long-haul ionospheric communication within the HF band stem from the variability of frequency-dependent group-delay dispersion and the power spectral density (PSD) of channel interference. To enable communication systems to adapt to the instantaneous condition of a frequency channel, diagnostic methods based on compact active radio sensors built with software-defined radio (SDR) technology are increasingly being deployed. These methods produce diagnostic data in the form of frequency-dependent power delay profiles (PDPs) together with the corresponding frequency-dependent PSDs of channel interference. In practical HF communication, such diagnostics are typically used to support frequency–time resource allocation. However, a significant challenge persists: the automatic extraction of information about the state of an ordered set of frequency channels and the associated radio-link parameters is constrained by the limited maturity of intelligent components in current processing frameworks. This shortcoming hinders the development of autonomous adaptation techniques for long-haul communication systems operating in a highly dynamic propagation environment. The objective of this study is to integrate methods for automatically filtering frequency-dependent PDP matrices contaminated by anthropogenic interference using machine-learning techniques with methods for determining the instantaneous characteristics of partial frequency channels and of the radio link as a whole. This paper presents an autonomous software-defined radio sensor designed for the diagnostics of long-haul HF communication channels. It also introduces a unified framework for primary processing (adaptive filtering and clustering) and secondary processing (estimation of inter-mode delays, identification of single- and multi-mode frequency regions, determination of guard-interval values, detection of channels with the highest signal-to-noise ratio, and extraction of the lower and upper cutoff frequencies of the link). The tasks addressed in this work include: advancing methods for filtering PDP matrices to reliably extract the signal from interference; applying a machine-learning algorithm for identifying received propagation modes based on k-means clustering to enable automatic estimation of radio-channel parameters; ensuring fully autonomous operation of the radio sensor; and conducting experimental verification of the developed primary and secondary processing algorithms for power delay profiles. These capabilities are essential for robust, automated estimation of the parameters of partial frequency channels and the overall characteristics of the HF communication link.

Ivanov D.V., Ivanov V.A., Ryabova M.I., Ovchinnikov V.V., Konkin N.A. Comprehensive method and tools for sensor-based diagnostics of long-haul HF communication channels to improve data rates and reliability. Electromagnetic waves and electronic systems. 2026. V. 31. № 1. P. 38−53. DOI: https://doi.org/10.18127/j15604128-202601-04 (in Russian)

1. Ivanov V., Ivanov D., Ryabova N., Ryabova M., Chernov A., Elsukov A. Computer filtering of images of ionograms of HF ionospheric radio communication paths and an algorithm for determining signal-to-noise ratio. Journal of Applied Engineering Science. 2018. V. 16. № 1. P. 116–124. DOI 10.5937/jaes16-11790.
2. Ivanov D.V., Ivanov V.A., Ryabova N.V., Belgibaev R.R., Yelsukov A.A., Kislitsyn A.A., Ovchinnikov V.V. Active-passive radio sensor for increasing the efficiency of HF communication. Electronics, photonics, and cyberphysical systems. 2023. V. 3. № 1. P. 7–12. (in Russian)
3. Furman W.N., Nieto J.W., Batts W.M. Wideband HF Channel Availability – Measurement Techniques and Results. 14th International Ionospheric Effects Symposium. Alexandria, Virginia, USA. 2015.
4. Belgibaev R.R., Ivanov D.V., Ivanov V.A., Ryabova N.V. Dependence of the Availability of Radio Channels on the Threshold Level of Man-Made Interferences. Wave Electronics and Its Application in Information and Telecommunication Systems. 2022. V. 5. № 1. P. 27–31.
5. Grozov V.P., Kurkin V.I., Ponomarchuk S.N. The techniques for processing and interpretation of ionosphere sounding data using the continuous chirp signal. Physical foundations of instrumentation. 2012. V. 1. № 3(4). P. 33–41. (in Russian)
6. Ivanov M.S., Shushkov A.V., Makarenko S.I. Increasing the data transmission rate in the air radio network of aircrafts controlling due to adaptive use of energy, signal and frequency resources of network. Part 1. Models and method of the data transmission rate increasing. Systems of Control, Communication and Security. 2023. № 1. P. 125–219. DOI 10.24412/2410-9916-2023-1-125-219. (in Russian)
7. Ivanov D.V. Methods and mathematical models for studying the propagation of complex decameter signals in the ionosphere and correcting their dispersion distortions: monograph. Yoshkar-Ola: Mari State Technical University. 2006. 266 p. (in Russian)
8. Ivanov D.V., Ivanov V.A., Elsukov A.A., Ryabova N.V., Ovchinnikov V.V., Isaev N.R. Method and adaptive algorithms for narrowband interferences mitigation and automatic signal detection in the problem of sensor diagnosis of multiple HF radio channels. Radiotekhnika. 2023. V. 87. № 12. P. 158−170. DOI 10.18127/j00338486-202312-17. (in Russian)
9. Ivanov V.A., Ovchinnikov V.V., Elsukov A.A., Chernov A.A. Filtration of Interferences Contaminating Wideband Signals Reception in Systems of Diagnosing Ionospheric HF Communication Links. Vestnik of Volga State University of Technology. Ser.: Radio Engineering and Infocommunication Systems. 2020. № 1(45). P. 18–29. DOI 10.25686/2306-2819.2020.1.18. (in Russian)
10. Ivanov D.V., Ivanov V.A., Ryabova N.V., Ryabova M.I., Ovchinnikov V.V., Yelsukov A.A. Algorithms for detecting useful signals against interference and their verification for a universal digital ionosonde created using SDR technology. Proceedings of the XV conference of young scientists "International Baikal youth scientific school of fundamental physics". Irkutsk: Institute of Solar-Terrestrial Physics of the Siberian Branch of the Russian Academy of Sciences. 2017. P. 181–183. (in Russian)
11. Grus J. K-means and hierarchical clustering with Python. O'Reilly Media, Inc. 2016.
12. Ivanov D., Ivanov V., Ryabova N., Konkin N., Ovchinnikov V. Method for Detecting Modes of Received Echo Signal on Image of Ionogram of Radio Sounding with the Use of Machine Learning. Wave Electronics and its Application in Information and Telecommunication Systems. St. Petersburg, Russian Federation. 2022. P. 1–5. DOI 10.1109/WECONF55058.2022.9803515.
13. OpenCV. Open Source Computer Vision Library: OpenCV modules. [Electronic resource] – Access mode: https://docs.opencv.org/4.x/, date of reference 23.10.2025.
14. Ivanov V.A., Ryabova N.V., Belgibaev R.R., Chernov A.A. Efficiency of HF Communication Modems Operating Over a Mid-latitude Long-Range Propagation Path. Systems of Signal Synchronization, Generating and Processing in Telecommunications. Minsk, Belarus. 2018. P. 1–5. DOI 10.1109/SYNCHROINFO.2018.8457021.
15. Warrington E.M., Stocker A.J. Measurements of the Doppler and multipath spread of HF signals received over a path oriented along the midlatitude trough. Radio Science. 2003. V. 38. № 5. P. 1–12.