Joint Path planning and Power Allocation of a Cellular-Connected UAV
  using Apprenticeship Learning via Deep Inverse Reinforcement Learning
Alireza Shamsoshoara Fatemeh Lotfi Sajad Mousavi Fatemeh Afghah Ismail Guvenc
This paper investigates an interference-aware joint path planning and power
allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in
a sparse suburban environment. The UAV's goal is to fly from an initial point
and reach a destination point by moving along the cells to guarantee the
required quality of service (QoS). In particular, the UAV aims to maximize its
uplink throughput and minimize the level of interference to the ground user
equipment (UEs) connected to the neighbor cellular BSs, considering the
shortest path and flight resource limitation. Expert knowledge is used to
experience the scenario and define the desired behavior for the sake of the
agent (i.e., UAV) training. To solve the problem, an apprenticeship learning
method is utilized via inverse reinforcement learning (IRL) based on both
Q-learning and deep reinforcement learning (DRL). The performance of this
method is compared to learning from a demonstration technique called behavioral
cloning (BC) using a supervised learning approach. Simulation and numerical
results show that the proposed approach can achieve expert-level performance.
We also demonstrate that, unlike the BC technique, the performance of our
proposed approach does not degrade in unseen situations.