Аlgorithm for Spectrum Sensing and Signal Selection by External Parameters



radio frequency spectrum, signal selection, external parameters, radio monitoring, associative array


For modern radio monitoring, a panoramic view of a wide frequency band and signal selection is its most important part. The constant growth of the number of radio electronic devices and the expansion of the instantaneous bandwidth of analysis in modern radio receiving devices leads to the fact that a significant number of analog and digital signals can be observed at the same time. Automatic adaptation of radio monitoring system to further signal processing is possible due to preliminary signal selection. The goal of this research is to develop an algorithm for signals selection in panoramic radio monitoring systems by their external parameters. The essence of proposed algorithm is to detect occupied bands of radio frequency spectrum, estimate center frequency and bandwidth of each channel, noise level and signal-to-noise ratio. Creation of frequency channels allows for signal filtering and estimation of pulse durations, as well as occupancy of each channel. Estimates of parameter for each signal fragment and frequency channel are recorded in associative arrays, which simplifies further signal selection. Due to variability of noise and propagation channel, estimates of signal parameters for each signal fragment are random variables. To obtain reliable estimates of signal center frequency and bandwidth, they are further grouping. Array of data can be accessed both by frequency channel number (table rows) and by signal parameters (keys), which are table column headers. Associative relationships between data provide flexible signals filtering by any combination of parameters. To test developed algorithm, we analyzed frequency band of 933-953 MHz and used the DataFrame Multi Index data container of Pandas package of Python programming language. This structure provides multi-level indexing, flexible access to data, and a wide range of tools for their processing and modifying. Developed algorithm can be used in existing and future radio monitoring systems for radio electronic devices identification and databases creation.

Author Biography

M. V. Buhaiov , S. P. Korolov Zhytomyr Military Institute, Zhytomyr, Ukraine

Candidate of Engineering Sciences, Senior Researcher



Liang Y.-C. (2020). Dynamic Spectrum Management. From Cognitive Radio to Blockchain and Artificial Intelligence. Springer, 180 р. doi: 10.1007/978-981-15-0776-2.

Saber M. et. al. (2020). Spectrum Sensing for Smart Embedded Devices in Cognitive Networks using Machine Learning Algorithms. Procedia Computer Science, Vol. 176, pp. 2404–2413. doi: 10.1016/j.procs.2020.09.311.

Zhang Y. et al. (2017). A Spectrum Sensing Method Based on Signal Feature and Clustering Algorithm in Cognitive Wireless Multimedia Sensor Networks. Advances in Multimedia, Vol. 2017, 11 p. doi: 10.1155/2017/2895680.

Franco H., Cobo-Kroenke C., Welch S., Graciarena M. (2020). Wideband Spectral Monitoring Using Deep Learning. Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning (WiseML2020), pp.19–24. doi: 10.1145/3395352.3402620.

Tekbiyik K. et al. (2021). Spectrum Sensing and Signal Identification with Deep Learning based on Spectral Correlation Function. IEEE Transactions on Vehicular Technology, Vol. 70, Iss. 10, pp. 10514-10527. doi: 10.1109/TVT.2021.3109236.

Tekbiyik K. et al. (2019). Multi-Dimensional Wireless Signal Identification Based on Support Vector Machines. IEEE Access, Vol. 7, pp. 138890-138903. doi: 10.1109/ACCESS.2019.2942368.

Jeevangi S., Jawaligi S., Patil V. (2022). Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks. Journal of Telecommunications and Information Technology, Vol. 4, pp. 21-31. doi: 10.26636/jtit.2022.164922.

Bedir O., Ekti A. R., Ozdemir M. K. (2023). Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach. Electronics, Vol. 12, 18 p. doi: 10.3390/electronics12194183.

Bari F., Agrawal P., Chatterjee B., Sen S. (2022). Statistical Analysis Based Feature Selection Enhanced RF-PUF With >99.8% Accuracy on Unmodified Commodity Transmitters for IoT Physical Security. Frontiers in Electronics, Vol. 3, 14 p. doi: 10.3389/felec.2022.856284.

Baldini G., Chareau J.-M., Bonavitacola F. (2021). Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning. Entropy, Vol. 23, 24 p. doi: 10.3390/e23121611.

Zayen B., Hayar A., Kansanen K. (2009). Blind Spectrum Sensing for Cognitive Radio Based on Signal Space Dimension Estimation. IEEE International Conference on Communications, pp. 1-5. doi: 10.1109/ICC.2009.5198794.

Yang M., Shao X., Xue G. et al. (2021). Big data theory based spectrum sensing algorithm for the satellite cognitive radio network. Wireless Networks, 9 p. doi: 10.1007/s11276-021-02808-7.

Zheng S. et al. (2018). Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges. IEEE Access, Vol. 6, pp. 55907-55922. doi: 10.1109/ACCESS.2018.2872769.

Recommendation ITU-R SM.1600-3. Technical identification of digital signals SM Series Spectrum management. (2017). ITU, 25 р.

Recommendation ITU-R SM.443 – Bandwidth measurement at monitoring stations.

Handbook. Spectrum monitoring. (2011). ITU Radiocommunication Bureau, 678 р.

Cook C. E., Bernfeld M. (1993). Radar Signals: An Introduction to Theory and Applications. Artech House, Inc.: Norwood, MA, USA. 552 p.

VanderPlas J. (2017). Python Data Science Handbook. Essential Tools for Working with Data. O’Reilly Media. 647 p.




How to Cite

Buhaiov , M. V. (2024) “Аlgorithm for Spectrum Sensing and Signal Selection by External Parameters”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, (95), pp. 5-15. Available at: https://radap.kpi.ua/radiotechnique/article/view/1975 (Accessed: 22April2024).



Telecommunication, navigation, radar systems, radiooptics and electroacoustics

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