Ming Zhao

Accurate performance prediction is essential for wireless network optimization and management. Graph neural networks (GNNs) as the state-of-the-art method for network modeling, leverage the pairwise relationships in graph structure of wireless networks to propagate information. However, network performance variations are influenced by diverse environmental factors, such as the operational states of nodes within the same access point (AP) network and the conditions for nodes and links traversed by flows. The lack of many-to-many high-order information will lead to the decline of prediction accuracy. Therefore, we propose HGNet, a wireless network performance prediction model based on hypergraph learning, which constructs multi-type hyperedge groups with hyperedge characteristics and typed-weighted hypergraph message passing mechanism with temporal dynamics. Furthermore, to support privacy-preserving multi-domain deployment, we propose FedHGT, a personalized federated hypergraph structure-aware optimization strategy that addresses higher-order heterogeneity in federated hypergraph training, including the multi-category of nodes and multi-connectivity of hyperedges. The server performs adaptive weighted aggregation based on the contribution and connectivity of hyperedges. We evaluate the performance of HGNet and FedHGT in ns-3 and the results demonstrate that they outperform state-of-the-art baselines by average margins of 15.9% and 5.6%, respectively.

As global demand for efficient network services increases, the limitations of traditional terrestrial wireless networks are becoming more apparent. Space-air-ground integrated networks (SAGIN) have emerged as a promising solution to advance next-generation network infrastructure. However, SAGIN faces significant security challenges—including the lack of a robust security architecture, trust and data reliability issues in multi-hop transmissions, and the need to enhance network performance without compromising security—rendering traditional boundary-based defenses inadequate. Therefore, we propose SECURELINK, a decentralized zero-trust architecture tailored for SAGIN, which replaces traditional perimeter defenses with a ”never trust, always verify” approach. This approach strengthens network security and flexibility through continuous verification and adherence to the principle of least privilege. SECURELINK integrates blockchain technology to establish a multi-layered security verification and data processing scheme, addressing the dynamic and decentralized features of SAGIN. Additionally, we introduce DFRIO, a zero-trust traffic offloading method based on decentralized federated reinforcement learning and blockchain, designed to enhance network performance within maintaining security. Simulation results demonstrate that our solution significantly enhances SAGIN’s defense capability without compromising network stability, outperforming the two baseline schemes by 18.3% and 42.6%, respectively.

Federated learning (FL) relies on timely participation of multiple devices, yet the round time is often dominated by the slowest user under heterogeneous wireless conditions. In multi-base-station (multi-BS) environments with nonlinear concurrent uplink and diverse backhaul capacities, selecting an appropriate set of participants becomes a challenging combinatorial optimization problem. We cast the problem as a round-time–centric optimization with reference-latency-scaled fairness penalties to enforce diversity, cohort size, and BS load balance. To solve it efficiently, we propose a Breakout Local Search (BLS) solver that couples k-opt local refinement with approximate screening and adaptive three-mode perturbations under a wall-clock budget, enabling efficient exploration–exploitation. Extensive simulations under hotspot and throttled-backhaul scenarios show that the proposed method reduces round time by 80–90% compared with particle swarm optimization (PSO) and Random, and by 29–75% compared with Greedy and simulated annealing (SA), while lowering the overall objective by 80–84% relative to PSO and Random and 50–67% relative to Greedy and SA, across various diversity weights and target participant sizes. The results highlight the effectiveness of our BLS method in FL participant selection under realistic and difficult network conditions.

Network device and system health management is the foundation of modern network operations and maintenance. Traditional health management methods, relying on expert identification or simple rule-based algorithms, struggle to cope with the heterogeneous networks (HNs) environment. Moreover, current state-of-the-art distributed fault diagnosis methods, which utilize specific machine learning techniques, lack multi-scale adaptivity for heterogeneous device information, resulting in unsatisfactory diagnostic accuracy for HNs. In this paper, we develop an LLM-assisted end-to-end intelligent network health management framework. The framework first proposes a multi-scale data scaling method based on unsupervised learning to address the multi-scale data problem in HNs. Secondly, we combine the semantic rule tree with the attention mechanism to propose a Multi-Scale Semanticized Anomaly Detection Model (MSADM) that generates network semantic information while detecting anomalies. Finally, we embed a chain-of-thought-based large-scale language model downstream to adaptively analyze the fault diagnosis results and create an analysis report containing detailed fault information and optimization strategies. We compare our scheme with other fault diagnosis models and demonstrate that it performs well on several metrics of network fault diagnosis.

Integrating Time-Sensitive Networking (TSN) with 5G enables Industrial IoT (IIoT) systems to achieve low latency and high reliability. However, deterministic cross-domain scheduling remains challenging due to strict synchronization demands and wireless channel variations. This paper proposes an asynchronous scheduling mechanism that consists of Ensemble Deep Reinforcement Learning and Online Asynchronous Flow Scheduling. The former integrates three reinforcement learning agents to generate a near-optimal Gate Control List (GCL) in complex asynchronous network environments, and the latter reallocates time-slot paths online based on the GCL to make fuller use of idle slot resources. Extensive OMNeT++ simulations show that the proposed asynchronous scheduling mechanism efficiently utilizes network resources, reduces delay from wireless fluctuations and asynchrony, and improves throughput.