Received 23.07.2025, Revised 28.10.2025, Accepted 23.12.2025

Intelligent frequency management in FANET: Fuzzy logic/routing and adaptive frequency hopping

Roman Zaivyi, Volodymyr Pavlysh

The study aimed to experimentally evaluate the effectiveness of intelligent frequency management in swarm networks of unmanned aerial vehicles (UAVs) using fuzzy logic and adaptive frequency hopping. The object of analysis was three frequency control methods: fixed frequency, classical frequency hopping, and the proposed adaptive method, which combines fuzzy logic decision making with context-dependent routing. The research was conducted in a MATLAB R2024a and Python 3.12 simulation environment on a model of five UAVs moving within an area of 1000×1000 m, incorporating changes in topology, signal level, signal-to-noise ratio, and energy characteristics of the nodes. The results demonstrated that the developed adaptive method provides the highest communication efficiency among the approaches studied. The packet delivery rate remained at 0.93-0.95 even in the presence of narrowband interference, which is 25-30% higher than the basic methods. The average end-to-end transmission delay decreased to 43 ms compared to 61 ms in the classic frequency hopping scheme and 78 ms in fixed mode. Power consumption decreased by 12-19%, and the average switching frequency was halved (≈ 2 times/s compared to 4.2 times/s in the classic mode), which indicates the optimisation of the controller’s operation. Statistical analysis confirmed the significant impact of the method type on all key communication performance indicators (p < 0.05), which confirms the reliability of the results obtained and the reproducibility of the system in a series of simulation experiments. The proposed approach provides autonomous optimisation of data transmission routes and maintenance of a stable communication channel even in dynamic environments, which creates prospects for the development of a new generation of intelligent UAV networks focused on real-time monitoring, reconnaissance and coordination tasks. The research results can be used by developers of unmanned systems, communications engineers and network technology specialists to create more interference-resistant, energy-efficient and self-learning communication systems

unmanned aerial vehicles; swarm networks; radio module; energy efficiency; packet delivery ratio; node mobility
41-53
Zaivyi, R., & Pavlysh, V. (2025). Intelligent frequency management in FANET: Fuzzy logic/routing and adaptive frequency hopping. Information Technologies and Computer Engineering, 22(3), 41-53. https://doi.org/10.31649/vitce/3.2025.41

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