Graph Neural Network Aided Deep Reinforcement Learning for Resource Allocation in Dynamic Terahertz UAV Networks

TL;DR

This paper introduces GLOVE, a graph neural network aided deep reinforcement learning algorithm designed to optimize resource allocation in dynamic Terahertz UAV networks, achieving higher efficiency and robustness.

Contribution

The paper proposes a novel GNN-aided DRL method called GLOVE for joint power and antenna resource allocation in dynamic THz UAV networks, addressing non-convexity and NP-hardness.

Findings

GLOVE achieves higher resource efficiency and lower latency than benchmark schemes.

GLOVE maintains zero packet loss during training, demonstrating robustness.

Experimental results validate GLOVE's superior performance in dynamic UAV networks.

Abstract

Terahertz (THz) unmanned aerial vehicle (UAV) networks with flexible topologies and ultra-high data rates are expected to empower numerous applications in security surveillance, disaster response, and environmental monitoring, among others. However, the dynamic topologies hinder the efficient long-term joint power and antenna array resource allocation for THz links among UAVs. Furthermore, the continuous nature of power and the discrete nature of antennas cause this joint resource allocation problem to be a mixed-integer nonlinear programming (MINLP) problem with non-convexity and NP-hardness. Inspired by recent rapid advancements in deep reinforcement learning (DRL), a graph neural network (GNN) aided DRL algorithm for resource allocation in the dynamic THz UAV network with an emphasis on self-node features (GLOVE) is proposed in this paper, with the aim of resource efficiency (RE) maximization. When training the allocation policy for each UAV, GLOVE learns the relationship between this UAV and its neighboring UAVs via GNN, while also emphasizing the important self-node features of this UAV. In addition, a multi-task structure is leveraged by GLOVE to cooperatively train resource allocation decisions for the power and sub-arrays of all UAVs. Experimental results illustrate that GLOVE outperforms benchmark schemes in terms of the highest RE and the lowest latency. Moreover, unlike the benchmark methods with severe packet loss, GLOVE maintains zero packet loss during the entire training process, demonstrating its better robustness under the highly dynamic THz UAV network.