_:n8e6cbaa04a7e43e1abc6165c2c30dd6dxb0
<http://schema.org/description>
"In different situations, like disaster communication and network connectivity\nfor rural locations, unmanned aerial vehicles (UAVs) could indeed be utilized\nas airborne base stations to improve both the functionality and coverage of\ncommunication networks. Ground users can employ mobile UAVs to establish\ncommunication channels and deliver packages. UAVs, on the other hand, have\nrestricted transmission capabilities and fuel supplies. They can't always cover\nthe full region or continue to fly for a long time, especially in a huge\nterritory. Controlling a swarm of UAVs to yield a relatively long communication\ncoverage while maintaining connectivity and limiting energy usage is so\ndifficult. We use modern deep reinforcement learning (DRL) for UAV connectivity\nto provide an innovative and extremely energy-efficient DRL-based algorithm.\nThe proposed method: 1) enhances novel energy efficiency while taking into\naccount communications throughput, energy consumption, fairness, and\nconnectivity; 2) evaluates the environment and its dynamics; and 3) makes\njudgments using strong deep neural networks. For performance evaluation, we\nhave performed comprehensive simulations. In terms of energy consumption and\nfairness, simulation results show that the DRL-based algorithm consistently\noutperforms two commonly used baseline techniques."
<http://api.deepai.org/publication/3d-uav-trajectory-design-for-fair-and-energy-efficient-communication-a-deep-reinforcement-learning-technique>