  Modern communication systems need to fulfill multiple and often conflicting
objectives at the same time. In particular, new applications require high
reliability while operating at low transmit powers. Moreover, reliability
constraints may vary over time depending on the current state of the system.
One solution to address this problem is to use joint transmissions from a
number 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 scheme
for an unmanned aerial vehicle (UAV) communication system with varying
communication reliability requirements. In particular, the proposed scheme aims
to minimize the total transmit power of all BSs while achieving an outage
probability that is less than a tolerated threshold. This threshold varies over
time, e.g., when the UAV enters a critical zone with high-reliability
requirements. Our results show that the proposed learning scheme uses dynamic
power allocation to meet varying reliability requirements, thus effectively
conserving energy.
