A Novel Joint DRL-Based Utility Optimization for UAV Data Services

TL;DR

This paper introduces a joint deep reinforcement learning approach using DQN and DDPG algorithms to optimize resource allocation in UAV-assisted communication networks, significantly increasing user service capacity.

Contribution

It presents a novel combined DRL framework for dynamic bandwidth and power allocation in UAV networks, improving efficiency over traditional static methods.

Findings

Up to 41% increase in users served.

Effective adaptation to fading conditions.

Dynamic resource management outperforms static allocation.

Abstract

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.