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Modern communication systems need to fulfill multiple and often conflictingobjectives at the same time. In particular, new applications require highreliability while operating at low transmit powers. Moreover, reliabilityconstraints may vary over time depending on the current state of the system.One solution to address this problem is to use joint transmissions from anumber of base stations (BSs) to meet the reliability requirements. However,this approach is inefficient when considering the overall total transmit power.In this work, we propose a reinforcement learning-based power allocation schemefor an unmanned aerial vehicle (UAV) communication system with varyingcommunication reliability requirements. In particular, the proposed scheme aimsto minimize the total transmit power of all BSs while achieving an outageprobability that is less than a tolerated threshold. This threshold varies overtime, e.g., when the UAV enters a critical zone with high-reliabilityrequirements. Our results show that the proposed learning scheme uses dynamicpower allocation to meet varying reliability requirements, thus effectivelyconserving energy.

Reinforcement Learning Based Dynamic Power Control for UAV Mobility Management