  In different situations, like disaster communication and network connectivity
for rural locations, unmanned aerial vehicles (UAVs) could indeed be utilized
as airborne base stations to improve both the functionality and coverage of
communication networks. Ground users can employ mobile UAVs to establish
communication channels and deliver packages. UAVs, on the other hand, have
restricted transmission capabilities and fuel supplies. They can't always cover
the full region or continue to fly for a long time, especially in a huge
territory. Controlling a swarm of UAVs to yield a relatively long communication
coverage while maintaining connectivity and limiting energy usage is so
difficult. We use modern deep reinforcement learning (DRL) for UAV connectivity
to provide an innovative and extremely energy-efficient DRL-based algorithm.
The proposed method: 1) enhances novel energy efficiency while taking into
account communications throughput, energy consumption, fairness, and
connectivity; 2) evaluates the environment and its dynamics; and 3) makes
judgments using strong deep neural networks. For performance evaluation, we
have performed comprehensive simulations. In terms of energy consumption and
fairness, simulation results show that the DRL-based algorithm consistently
outperforms two commonly used baseline techniques.
