Статистична оптимізація стабільності БПЛА через параметри мережі 5G

Background: Drones, or Unmanned Aerial Vehicles (UAVs), have gained popularity across diverse industries due to their versatility and efficiency. However, achieving optimal stability during flight remains a challenge, particularly in dynamic environments. The advent of 5G technology, with its high-speed, low-latency capabilities, provides an opportunity to enhance drone control and stability through real-time data transmission and responsive adjustments. Objective: This study explores the application of statistical methods to optimize drone balance via 5G connectivity, focusing on key network parameters such as latency, signal strength, and bandwidth utilization. Methods: A series of flights were conducted under varying environmental conditions to gather data on drone stability metrics, including roll, pitch, and yaw. Statistical analyses, including regression models and time-series analysis, were applied to assess the relationship between 5G parameters and drone balance. Optimization algorithms were then used to adjust network settings dynamically, aiming to improve stability in real-time. Results: Findings indicate that latency and signal strength are significant predictors of drone stability, with lower latency and optimized signal strength correlating with improved balance. Additionally, bandwidth optimization contributed to smoother flight control by prioritizing essential data flows. Conclusion: Statistical methods play a crucial role in maximizing the benefits of 5G technology for drone stability. By fine-tuning 5G parameters, drones can maintain better balance, enhancing their overall operational efficiency and reliability.