DRL meets GNN to improve QoS in Tactical MANETs
Johannes F. Loevenich, Roberto Rigolin F. Lopes · 2024
This paper proposes a hybrid AI model combining Graph Neural Network (GNN) and Deep Reinforcement Learning (DRL) to improve QoS in modern communication systems deployed to tactical networks. Our methodology consists of three interacting agents: an environment builder agent responsible for generating complex network graph environments, a DRL agent situated within the control plane that possesses a global view of the current network state and makes decisions based on information gathered from various layers of the multi-layer tactical system, and an adversary designed to improve the robustness of the DRL agent. Our initial results indicate that enabling GNNs and DRL together with adversarial training is a promising solution for enhancing the Quality of Service (QoS) of modern tactical communication systems operating in hostile environments that may host active adversaries.