Scheduling over untrusted 6G networks: An HGNN-augmented reinforcement learning approach for joint delay-security guarantees
Author Identifier (ORCID)
Abstract
In next-generation intelligent networks, heterogeneous devices will operate in dynamic and untrusted environments. Machine learning-based task scheduling becomes increasingly susceptible to adversarial attacks, shifting trust landscapes. An urgent need arised for robust and trustworthy resource management mechanisms. To enable time-sensitive and secure task scheduling, we propose a Heterogeneous Graph Neural Network–augmented Deep Reinforcement Learning (HGNN-DRL) framework. The framework models multi-step, multi-device scheduling as a knowledge graph to account for various practical constraints. Based on the knowledge graph, an HGNN is designed to extract topological embeddings, which jointly capture task dependencies and device security states. To drive decision-making, we introduce an Entropy-Regularized Double Deep Q-Network (ER-DDQN) strategy, which decouples action selection from value estimation through the DDQN architecture. This decoupling improves training stability by separating target action selection from Q-value evaluation. Meanwhile, policy entropy is maximized to enhance exploration and improve robustness in dynamic environments. A Lyapunov-stability analysis shows that ER-DDQN ensures stable scheduling performance under dual constraints of delay and security. Simulations demonstrate that HGNN-DRL outperforms state-of-the-art A2C and TRPO algorithms by 22% and 29%, respectively, in task throughput, while achieving higher security compliance and significantly reduced end-to-end delay.
Keywords
double deep Q-network (DDQN), heterogeneous graph neural network (GNN), untrusted networks
Document Type
Journal Article
Date of Publication
1-1-2026
E-ISSN
23327731
Volume
12
Publication Title
IEEE Transactions on Cognitive Communications and Networking
Publisher
IEEE
School
School of Engineering
Copyright
subscription content
First Page
11567
Last Page
11581
Recommended Citation
Feng, J., Zou, S., Liwang, M., Li, K., Ni, W., & Jamalipour, A. (2026). Scheduling over untrusted 6G networks: An HGNN-augmented reinforcement learning approach for joint delay-security guarantees. IEEE Transactions on Cognitive Communications and Networking, 12, 11567–11581. https://doi.org/10.1109/TCCN.2026.3725070