  In this paper, we propose a novel joint deep reinforcement learning
(DRL)-based solution to optimize the utility of an uncrewed aerial vehicle
(UAV)-assisted communication network. To maximize the number of users served
within the constraints of the UAV's limited bandwidth and power resources, we
employ deep Q-Networks (DQN) and deep deterministic policy gradient (DDPG)
algorithms for optimal resource allocation to ground users with heterogeneous
data rate demands. The DQN algorithm dynamically allocates multiple bandwidth
resource blocks to different users based on current demand and available
resource states. Simultaneously, the DDPG algorithm manages power allocation,
continuously adjusting power levels to adapt to varying distances and fading
conditions, including Rayleigh fading for non-line-of-sight (NLoS) links and
Rician fading for line-of-sight (LoS) links. Our joint DRL-based solution
demonstrates an increase of up to 41% in the number of users served compared to
scenarios with equal bandwidth and power allocation.
