Uncertainty-Aware Multi-Agent Reinforcement Learning for Anti-Interference Trajectory Planning of Cellular-Connected UAVs

Cellular-connected unmanned aerial vehicles (C-UAVs) will be an integral component of future wireless networks. Thanks to the mobility and maneuverability of UAVs, we can transform the interference management and route scheduling problems of C-UAVs into an anti-interference trajectory planning problem, aiming to jointly minimize the UAV mission time and transmission outage time. However, none of the existing methods have taken both the spatio-temporal uncertainty of interference sources and multi-UAV trajectory planning into consideration. To address this issue, we propose a novel method, referred to as uncertainty-aware multi-agent reinforcement learning (UA-MARL), for anti-interference trajectory planning of C-UAVs. In UA-MARL, a transmission outage probability (TOP) has been introduced to improve the robustness of the model. A transmission outage probability experience memory (TOPEM) has been designed to increase sample efficiency and reduce inference time. MARL algorithms integrated with an adaptive post-decision state (PDS) have been introduced to accelerate the convergence and stabilize the training. Experimental results show that UA-MARL outperforms baselines in average reward, convergence efficiency, and convergence stability. Furthermore, we find that higher residential density and wider considered area will lead to a decrease in training efficiency and stability.