Abstract
Unmanned aerial vehicles (UAVs) as an Aerial Base Station (ABS) are the enabler in the provisioning of emergency communication services. However, ABS unplanned deployment creates interference from the neighboring co-channel base station, which hinders meeting the required quality-of-service (QoS) requirements and the minimum rate of users. Hence, the ABS deployment and its power allocation require a machine learning-based solution xthat can plan in real-time to enhance the users’ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max-min sum-rate</i> . We propose the reinforcement learning-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> greedy algorithm to solve the max-min optimization problem. The simulation results validate the proposal by achieving around 2.3 bps/Hz high <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimum sum-rate</i> compared to the conventional water filling algorithm at the same ABS altitude.
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