Throughput Capacity of RF Sensor for Unmanned Aerial Vehicle
DOI:
https://doi.org/10.64915/RADAP.2026.103.%25pKeywords:
radio frequency emitter, RF sensor, throughput capacity, Poisson process, spatial density, signal flow, unmanned aerial vehicleAbstract
Usage of small unmanned aerial vehicles (UAVs) for spectrum sensing, especially in urban areas, has numerous advantages over the use of ground-based stations for radio frequency (RF) emitters detection and location. In order to develop spectrum sensing equipment for UAVs, it is necessary to establish a number of requirements for it. One of the main requirements is the necessary throughput capacity. The purpose of the article is to improve the methodological apparatus to establish requirements for UAVs spectrum sensing equipment. To describe the density of RF emitters distribution, it is proposed to use a nonhomogeneous Poisson spatial process in combination with parametric or nonparametric distribution functions. The density function of this distribution reflects the average number of RF emitters that are within energy accessibility and can be detected. Using a quantile of a given Poisson distribution level, in which the density function is used as a parameter, allows to estimate maximum number of RF emitters. The signal flow from each RF emitter is described using a nonstationary Poisson process. The moments of time of broadcast and the duration of signal emission are exponentially distributed. Estimates of the average intensity of RF emitters during analyzed time interval of a given frequency band for a single-channel multi-antenna system have been obtained. The methodology for estimating the required throughput capacity of RF sensor and recommendations for using the proposed methodological apparatus in conditions of a priori uncertainty regarding the density of RF emitters distribution and signal flow intensity are presented. Using the values of the maximum number of RF emitters within the energy availability range for the entire spectrum sensing area, the average intensity of RF emitters, and the analysis time of the instantaneous frequency band, it was obtained an estimate of required throughput capacity of RF sensor.
References
1. Hoffmann F., Schily H., Krestel M. et al. (2023). Non-myopic Sensor Path Planning for Emitter Localization with a UAV. 26th International Conference on Information Fusion, 8 p. doi: 10.23919/FUSION52260.2023.10224174.
2. Kwon H., Guvenc I. (2023). RF Signal Source Search and Localization Using an Autonomous UAV with Predefined Waypoints. IEEE 97th Vehicular Technology Conference (VTC2023-Spring), pp. 1-6. doi: 10.1109/VTC2023-Spring57618.2023.10200783.
3. Buhaiov M. V. (2024). Matematychna model pryiniatoho syhnalu panoramnym zasobom radiomonitorynhu na bezpilotnomu litalnomu aparati [Mathematical Model of the Received Signal by Panoramic Means of Radio Monitoring on an Unmanned Aerial Vehicle]. Vseukr. mizhvidomchyi nauk.-tekhn. zbirnyk «Radiotekhnika» [All-Ukrainian Interdepartmental Scientific and Technical Journal «Radiotekhnika»], № 219, pp. 82-91. doi: 10.30837/rt.2024.4.219.09.
4. Chen F., Rezatofighi S. H., Ranasinghe D. C. (2024). GyroCopter: Differential Bearing Measuring Trajectory Planner for Tracking and Localizing Radio Frequency Sources. Computer Science. Robotics, 9 р. doi: 10.48550/arXiv.2410.13081.
5. Buhaiov M. V. (2025). Unmanned aerial vehicle flight speed optimization for spectrum sensing. Problems of Construction, Testing, Application and Operation of Complex Information Systems, Zhytomyr: ZVI, Iss. 28 (І), pp. 5-15. doi: 10.46972/2076-1546.2025.28.01.
6. Keeler H. P., Ross N., Xia A. (2018). When do wireless network signals appear Poisson? Bernoulli, Vol. 24, Iss. 3, pp. 1973-1994, doi: 10.3150/16-BEJ917.
7. Suryaprakash V., Møller J., Fettweis G. (2015). On the Modeling and Analysis of Heterogeneous Radio Access Networks Using a Poisson Cluster Process. IEEE Transactions on Wireless Communications, Vol. 14, Iss. 2, pp. 1035-1047, doi: 10.1109/TWC.2014.2363454.
8. Heath R. W., Kountouris M., Bai T. (2013). Modeling Heterogeneous Network Interference Using Poisson Point Processes. IEEE Transactions on Signal Processing, Vol. 61, Iss. 16, pp. 4114-4126. doi: 10.1109/TSP.2013.2262679.
9. Chen C., Basnayaka D., Haas H. (2015). Downlink SINR Statistics in OFDM-Based Optical Attocell Networks with a Poisson Point Process Network Model. IEEE Global Communications Conference, pp. 1-6, doi: 10.1109/GLOCOM.2015.7417221.
10. Guo A., Zhong Y., Zhang W., Haenggi M. (2016). The Gauss–Poisson Process for Wireless Networks and the Benefits of Cooperation. IEEE Transactions on Communications, Vol. 64, Iss. 5, pp. 1916-1929. doi: 10.1109/TCOMM.2016.2550525.
11. Yazdanshenasan Z., Dhillon H. S., Afshang M., Chong P. H. J. (2016). Poisson Hole Process: Theory and Applications to Wireless Networks. IEEE Transactions on Wireless Communications, Vol. 15, No. 11, pp. 7531-7546, doi: 10.1109/TWC.2016.2604799.
12. Kong H.-B. et al. (2017). Modeling and analysis of wireless networks using poisson hard-core process. IEEE International Conference on Communications (ICC), pp. 1-6, doi: 10.1109/ICC.2017.7997052.
13. How many Radio Frequency sensors do I need for my project? Cambridge Radio Frequency Systems (CRFS), access data: October, 2025.
14. Gul O. M., Demirekler M. (2017). Average Throughput Performance of Myopic Policy in Energy Harvesting Wireless Sensor Networks. Sensors, Vol. 17, Iss. 10, 2206, doi: 10.3390/s17102206.
15. Mishra D., De S. (2016). Achievable throughput in relay-powered RF harvesting cooperative sensor networks. 22nd National Conference on Communication, pp. 1-6, doi: 10.1109/NCC.2016.7561155.
16. Liu W. et al. (2012). On the Throughput Capacity of Wireless Sensor Networks With Mobile Relays. IEEE Transactions on Vehicular Technology, Vol. 61, No. 4, pp. 1801-1809. doi: 10.1109/TVT.2012.2188145.
17. Mahmoud H. H., Hafiz A. H., Fathy K. A., Abdellatif S. O. (2019). Throughput of Underwater Wireless Sensor Nodes with Energy Harvesting Capabilities Using RF and Optical Links. International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 8, No. 3, pp. 177-180. doi: 10.18178/ijeetc.8.3.177-180.
18. Sha M., Xie Y. (2016). The Study of Different Types of Kernel Density Estimators. 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016), Atlantis Press, pp. 332-336. doi: 10.2991/icence-16.2016.67.
19. Węglarczyk S. (2018). Kernel density estimation and its application. ITM Web of Conferences, Vol. 23, 8 p. doi: 10.1051/itmconf/20182300037.
20. Diggle P. J. (2014). Statistical Analysis of Spatial and Spatio-Temporal Point Patterns. Taylor & Francis Group, LLC, 3rd ed., 297 р.
21. Streit R. L. (2010). Poisson Point Processes. Imaging, Tracking, and Sensing. Springer Science+Business Media, LLC, 280 p.
22. Baddeley A. (2007). Spatial Point Processes and their Applications. Stochastic Geometry. Springer, Part of the book series: Lecture Notes in Mathematics, Vol 1892, Chapter, pp. 1–75, doi: 10.1007/978-3-540-38175-4_1.
23. Illian J. et al. (2008). Statistical Analysis and Modelling of Spatial Point Patterns. John Wiley & Sons Ltd, 557 p.
24. Kay S. M. (2013). Fundamentals of statistical signal processing: Practical algorithm development, Vol. 3. Prentice Hall, New Jersey, 403 p.
25. Ross S. M. (2019). Introduction to Probability Models, 12th ed., 826 p.
26. World Cell Towers.
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