  This letter is concerned with power control for a ultra-reliable and
low-latency communications (URLLC) enabled unmanned aerial vehicle (UAV) system
incorporated with deep neural network (DNN) based channel estimation.
Particularly, we formulate the power control problem for the UAV system as an
optimization problem to accommodate the URLLC requirement of uplink control and
non-payload signal delivery while ensuring the downlink high-speed payload
transmission. This problem is challenging to be solved due to the requirement
of analytically tractable channel models and the non-convex characteristic as
well. To address the challenges, we propose a novel power control algorithm,
which constructs analytically tractable channel models based on DNN estimation
results and explores a semidefinite relaxation (SDR) scheme to tackle the
non-convexity. Simulation results demonstrate the accuracy of the DNN
estimation and verify the effectiveness of the proposed algorithm.
