Unmanned aerial vehicles (UAVs) serving as aerial base stations can be
deployed to provide wireless connectivity to mobile users, such as vehicles.
However, the density of vehicles on roads often varies spatially and temporally
primarily due to mobility and traffic situations in a geographical area, making
it difficult to provide ubiquitous service. Moreover, as energy-constrained
UAVs hover in the sky while serving mobile users, they may be faced with
interference from nearby UAV cells or other access points sharing the same
frequency band, thereby impacting the system's energy efficiency (EE). Recent
multi-agent reinforcement learning (MARL) approaches applied to optimise the
users' coverage worked well in reasonably even densities but might not perform
as well in uneven users' distribution, i.e., in urban road networks with uneven
concentration of vehicles. In this work, we propose a density-aware
communication-enabled multi-agent decentralised double deep Q-network
(DACEMAD-DDQN) approach that maximises the total system's EE by jointly
optimising the trajectory of each UAV, the number of connected users, and the
UAVs' energy consumption while keeping track of dense and uneven users'
distribution. Our result outperforms state-of-the-art MARL approaches in terms
of EE by as much as 65% - 85%.

该论文提出了一种密度感知的通信多智能体去中心化双重深度 Q 网络方法，它最大化了每个无人机的轨迹、已连接用户数量和无人机的能耗，同时跟踪密集和不均匀的用户分布。该方法优于现有技术 65％-85％的能源效率。