TD3 algorithm stabilizes drone base stations against wind disturbances
Wind-induced drift disrupts UAV base stations; TD3 reinforcement learning maintains connectivity
Unmanned aerial vehicle (UAV)-mounted base stations are highly vulnerable to wind gusts and turbulence, which cause positional drift and degrade communication link quality, especially in emergency scenarios. A team led by Azim Akhtarshenas from the University of Oulu and Nokia Bell Labs proposes a deep reinforcement learning (DRL) framework based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Instead of relying on complex aerodynamic models, they treat wind as a stochastic kinematic perturbation, enabling the TD3 agent to learn adaptive control policies that maintain optimal coverage footprints. The approach prioritizes user-centric performance metrics under turbulent conditions, ensuring continuous service availability despite external disruptions.
Simulation results demonstrate that the TD3-based approach effectively compensates for wind-induced displacements and outperforms benchmark methods, including Proximal Policy Optimization (PPO), in terms of throughput stability and robustness in windy environments. The paper, published on arXiv in June 2026, highlights the potential for reliable UAV communication networks in disaster response or remote areas where traditional infrastructure is unavailable. By sidestepping aerodynamic modeling, this method offers a practical, data-driven solution for real-time trajectory optimization.
- TD3 model treats wind as stochastic perturbation, avoiding complex aerodynamic modeling
- Outperforms PPO in throughput stability and robustness under windy conditions
- Designed for emergency scenarios where continuous service availability is critical
Why It Matters
Reliable drone-based cellular networks during storms or disasters without expensive aerodynamic modeling.