Xiaorong Zhu

Semantic communications have emerged as a key paradigm for intelligent sixth-generation (6G) wireless networks, which aim to convey the meaning of information rather than accurate bit sequences. However, in open-space low Earth orbit (LEO) satellite links, the broadcast nature and wide beam coverage expose semantic transmissions to severe eavesdropping risks. This paper establishes a unified theoretical and algorithmic framework for secure semantic downlink transmission in satellite networks. In particular, we first develop an integrated mathematical model that couples the semantic representation process, physical-layer satellite propagation characteristics, and information-theoretic secrecy into a single analytical formulation. By defining a joint semantic security cost function, the antagonistic trade-off between semantic fidelity and secrecy capacity is quantitatively characterized under realistic power, beamforming, and propagation constraints. To balance semantic fidelity and information secrecy, a reinforcement-learning-based optimization framework is proposed, wherein an actor–critic agent learns optimal power allocation and semantic weighting strategies through continuous interaction with the environment. This learning-based optimization approach enables autonomous control without requiring explicit channel distribution knowledge or offline parameter tuning. Extended simulation results show that the proposed approach consistently enhances both semantic fidelity and secrecy performance compared with conventional power-control schemes and demonstrate its potential as a foundational architecture for secure and intelligent semantic communications in next-generation satellite networks.

Sudden disasters frequently disrupt terrestrial cellular infrastructure, severely degrading network capacity in affected regions and necessitating the rapid restoration of uplink connectivity for rescue and monitoring operations. To address this challenge, we propose a systematic capacity analysis framework for a three-tier emergency wireless network comprising a device access layer, an unmanned aerial vehicle (UAV) relay layer, and a backhaul layer. First, within the access layer, heterogeneous ground devices are modeled using a Poisson point process (PPP). We analyze the uplink transmissions under non-orthogonal multiple access (NOMA), deriving the coverage probability and ergodic capacity while accounting for imperfect successive interference cancellation (SIC). Next, in the relay layer, multiple concurrent flows are scheduled via time-frequency resource allocation across end-to-end UAV links, incorporating practical communication constraints and multihop packet-loss effects. To balance throughput and fairness, we introduce a joint resource allocation scheme that maximizes a novel satisfaction function. In the backhaul layer, the capacity between UAVs and base stations (BSs) is characterized under space-division multiple access (SDMA) with directional beamforming, explicitly modeling the impact of antenna-orientation jitter at both the UAV and BS sides. Finally, the overall end-to-end system capacity is formulated as the bottleneck capacity among the three tiers. Analytical and simulation results validate the accuracy of the proposed theoretical model. Furthermore, comparative evaluations demonstrate that the proposed resource allocation scheme for the UAV relay layer significantly improves allocation fairness among concurrent flows relative to conventional baselines.

Unmanned Aerial Vehicle networks, also known as Flying Ad Hoc Networks (FANETs), offer significant advantages in applications such as disaster response and emergency communications, owing to their rapid deployment, wide-area coverage, and strong survivability. Routing protocols are critical to FANETs performance, ensuring reliable communication through dynamic path maintenance and three-dimensional (3D) topology optimization. However, most existing protocols are designed primarily based on node mobility or network topology, often overlooking traffic distribution. This can lead to severe congestion in high-traffic areas, resulting in increased end-to-end delays, delayed route updates, and packet loss. While some existing methods address congestion through flow prediction, they frequently disregard topological relationships among nodes, reducing the accuracy of predictions. To address these challenges, this paper proposes a Multi-Metric Joint Prediction (MMJP) routing protocol based on a Spatio-Temporal Graph Convolutional Network (STGCN). MMJP jointly models traffic dynamics and topological structure to enhance prediction accuracy and congestion control. The proposed STGCN model exchanges information with neighboring nodes, aggregates their features, and learns spatio-temporal representations to forecast future node resource availability. The resulting predicted network state is used to select routes that avoid congestion-prone areas. Simulation results demonstrate that MMJP outperforms benchmark protocols, including Destination-Sequenced Distance Vector (DSDV), 3D Geographic Greedy Routing (GREEDY), and the predictive protocol OPAR. Specifically, MMJP achieves over 30$\%$ higher Packet Delivery Ratio and a 35$\%$ improvement in throughput compared to DSDV and GREEDY, and outperforms OPAR by approximately 10$\%$ in both metrics.

This research introduces a new intelligent routing and resource allocation algorithm called Graph Sample and Aggregate-Multi-Agent Proximal Policy Optimization (GraphSAGE-MAPPO), which targets dynamic wireless mesh networks like those present in emergency communications. Aiming to address the emergency communication scenario where the network topology changes dynamically and the introduction of Artificial Intelligence (AI) model training services leads to more diverse user services and more dynamic node resource capabilities, a three-dimensional mesh network intelligent routing and resource allocation algorithm, GraphSAGE-MAPPO, based on Graph Neural Networks (GNN) combined with Deep Reinforcement Learning (DRL), is proposed. During the training process, the algorithm first uses GNN as a network feature extraction module to extract the resource capabilities and link status indicators of the nodes, thereby generating a hidden feature vector representation for each backbone mesh node; then, the feature vectors of each node are combined with the arrival service flow as the state input of the distributed multi-agent DRL model, supporting efficient routing and resource allocation decisions for service flows with different user Quality of Service (QoS) requirements. Simulation results show that in the face of dynamically changing network environments and user needs, the GraphSAGE-MAPPO algorithm proposed in this thesis can flexibly adjust routing strategies to better meet the QoS requirements of various services and has good generalization performance for network topology and resource changes. These results demonstrate that the algorithm has good flexibility and scalability in large-scale, real-world wireless mesh network environments.

In the cloud–edge–end communication architecture of the new power system, heterogeneous perception services face a fundamental and long-standing demand–supply mismatch with multi-dimensional resources (computing, storage, spectrum/bandwidth, and power) under QoS constraints such as delay, reliability, and accuracy. To uniformly measure and minimize this mismatch under resource-limited and time-varying network conditions—thereby enabling precise and efficient perception—this paper proposes an intelligent perception-service efficiency evaluation and optimization method for electric power information and communication networks based on fit entropy. First, based on the theory of information entropy, the fit entropy is defined for the degree of matching between the requirements of perception services such as delay and reliability and the provision of resources. Then, based on the fit entropy, a three-layer matching model of business domain- logical domain- physical domain is constructed, and then a many-to-many matching optimization problem between the business, service function chain and physical device is formed. Furthermore, a dynamic hypergraph neural network based on the gated attention mechanism is designed to solve this problem, where the multi-type aware service requests are dynamically mapped to cross-domain hyperedges, and the fit entropy is used as the weight of the hyperedges to quantify the global fit among the three domains. The fit entropy is optimized by adaptively adjusting the hypergraph structure and the weight of the hyperedges. The simulation results show that this method can significantly improve the quality of service of perceptive services and effectively balance the utilization of network resources and service adaptability.

With the large-scale deployment of low earth orbit satellite network (LEO-SN), end-to-end (E2E) delay analysis under multi-traffic faces significant challenges from time-varying service capabilities, propagation delay-induced queue coupling, dynamic resource contention, etc. In this paper, we develop a martingale-based analytical framework to E2E delay violation probability in multi-hop, multi-traffic LEO-SN scenarios. First, bursty traffic arrivals are modeled through a Markov chain Monte Carlo process, while satellite-to-ground and inter-satellite link dynamics are captured through finite-state Markov channels, forming the basis for single-hop queuing analysis. We then extend stochastic network calculus to quantify queue coupling effects due to propagation delay and incorporate dynamic residual service allocation under a priority scheduling mechanism. To address the compounded complexity of multi-hop, multi-traffic interactions, we apply martingale theory and the stopping time theorem to derive closed-form upper bounds on backlog and delay violation probability. Furthermore, we introduce global and hierarchical stability conditions that characterize priority scheduling constraints and a scaling factor to reflect service heterogeneity across links. Extensive simulations validate the tightness of our bounds and demonstrate that, although priority scheduling significantly improves delay performance for higher-priority traffic, imbalanced link loads and transient service interruptions can amplify delay variability.

With the rapid expansion of data scale, compute-intensive tasks will become a core application of 6G networks. As Unmanned Aerial Vehicle (UAV) technology advances, UAVs can assist in task offloading for mobile edge computing by collaborating to overcome individual UAV limitations in battery life and computational capacity. Hence, in this paper, we propose a task offloading algorithm for multiple UAVs based on a temporal graph. We first formulate an optimization problem to minimize the total completion time of UAV swarm task offloading by classifying tasks and determining task priorities and subtask dependencies. To solve this problem, we introduce a temporal graph to simulate service nodes and task sequences in computing networks. It can reveal task execution priorities by calculating proximity indices, which indicate the ratio of physical distance to the sum of task weights, and determining timestamp offsets. In the following, to reduce unnecessary waiting and computation resource allocation risks, we transform the optimization problem into a directed acyclic graph connectivity problem, which identifies the fastest temporal paths for each UAV, forming a dedicated service network. Finally, we propose a two-stage matching algorithm that achieves optimal matching based on service node locations, statuses, task types, and offloading demands. Simulation results demonstrate that the algorithm performs exceptionally well, reducing task completion times and significantly outperforming other algorithms in terms of task utility.

To tackle the problems of large data volume, multiple devices, and difficult data acquisition in large-scale sensor networks, we propose a multi-UAV (Unmanned Aerial Vehicle) cooperative trajectory planning and data acquisition method based on the fusion of multi-traveling salesman problem (mTSP) and multi-agent reinforcement learning. Considering the factors such as the wide and uneven distribution of sensing nodes, limited transmission range, and limited energy of UAVs, firstly, large-scale sensing nodes perform intra-cluster data fusion processing by cluster heads, and multi-UAV cooperative trajectory planning acquires cluster head node data to balance the working time of each UAV, thereby improving energy efficiency. Then, the multi-UAV data acquisition problem is modeled as a mTSP, and an improved genetic algorithm with 2-opt optimization operator to optimize the sequence of data acquisition for multi-UAV, so that each UAV acquires the data of the cluster head nodes in its own area in the shortest time. To optimize the UAV’s trajectory based on the data acquisition sequence, we model it as a partially observable Markov decision process, train the model with the counterfactual multi - agent policy gradient method, and solve the agents’ belief allocation problem using the advantage function based on difference reward. Simulation results show that the proposed algorithm has good performance in terms of flight time, data acquisition and energy consumption.