Density-Aware Reinforcement Learning to Optimise Energy Efficiency in
  UAV-Assisted Networks
Babatunji Omoniwa Boris Galkin Ivana Dusparic
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%.