Umix: Sustainable Multi-UAV Coordination for Aerial-Terrestrial Networks

The deployment of Unmanned Aerial Vehicles (UAVs) alongside wireless communication networks as Aerial-Terrestrial Network (ATN) in vast and remote wilderness regions shows a growing trend in the current era. Simultaneously, the adoption of 6G technologies is imperative for the effective performance of ATN tasks, such as communication relaying, data transmission, and remote device management. However, the deployment of multiple UAVs in ATN faces challenges in energy management and coordination, particularly in rigorous environments where the power supply is restricted. To overcome these challenges, this work develops Umix, an innovative approach grounded in Deep Reinforcement Learning (DRL) method Qmix, engineered to refine the trajectories and operational dynamics of multiple UAVs. Umix is deployed to provide energy-efficient solutions for multiple UAVs, taking into account the limitations of battery capacity. It features an encoder-decoder architecture that adeptly captures the historical trajectory data of UAVs. Furthermore, it integrates a weight assignment network that is adept at evaluating and distributing tasks across the UAVs with high efficiency. Extensive experiments are conducted to demonstrate the advancement of the developed method Umix. The numerical results show that Umix can conserve more energy and time, which outperforms benchmarks under the settings of different amounts of UAVs and sensors.