Bo Ma

Resource management is one of the challenges in satellite networks due to their high mobility, wide coverage, long propagation distances, and stringent constraints on energy, communication, and computation resources. Traditional resource allocation approaches rely only on hard and rigid system performance metrics. Meanwhile, incentive mechanisms, which are based on game theory and auction theory, investigate systems from the "economic" perspective in addition to the "system" perspective. Particularly, incentive mechanisms are able to take into account rationality and other behavior of human users into account, which guarantees benefits/utility of all system entities, thereby improving the scalability, adaptability, and fairness in resource allocation. This paper presents a comprehensive survey of incentive mechanism design for resource management in satellite networks. The paper covers key issues in the satellite networks, such as communication resource allocation, computation offloading, privacy and security, and coordination. We conclude with future research directions including learning-based mechanism design for satellite networks.

The future of UAV interaction systems is evolving from engineer-driven to user-driven, aiming to replace traditional predefined Human-UAV Interaction designs. This shift focuses on enabling more personalized task planning and design, thereby achieving a higher quality of interaction experience and greater flexibility, which can be used in many fileds, such as agriculture, aerial photography, logistics, and environmental monitoring. However, due to the lack of a common language between users and the UAVs, such interactions are often difficult to be achieved. The developments of Large Language Models possess the ability to understand nature languages and Robots' (UAVs') behaviors, marking the possibility of personalized Human-UAV Interaction. Recently, some HUI frameworks based on LLMs have been proposed, but they commonly suffer from difficulties in mixed task planning and execution, leading to low adaptability in complex scenarios. In this paper, we propose a novel dual-agent HUI framework. This framework constructs two independent LLM agents (a task planning agent, and an execution agent) and applies different Prompt Engineering to separately handle the understanding, planning, and execution of tasks. To verify the effectiveness and performance of the framework, we have built a task database covering four typical application scenarios of UAVs and quantified the performance of the HUI framework using three independent metrics. Meanwhile different LLM models are selected to control the UAVs with compared performance. Our user study experimental results demonstrate that the framework improves the smoothness of HUI and the flexibility of task execution in the tasks scenario we set up, effectively meeting users' personalized needs.

The resource management is one of the challenges in satellite networks due to their high mobility, wide coverage, long propagation distances, and stringent constraints on energy, communication, and computational resources. Traditional resource allocation approaches rely only on hard and rigid system performance metrics. Meanwhile, incentive mechanisms, which are based on the game theory and auction theory, investigate systems from the “economic” perspective in addition to the “system” perspective. Particularly, incentive mechanisms are able to take into account rationality and other behavior of human users into account, which guarantees benefits/utility of all system entities, thereby improving the scalability, adaptability, and fairness in resource allocation. This article presents a comprehensive survey of incentive mechanism design for resource management in satellite networks. This article covers key issues in the satellite networks, such as communication resource allocation, computational offloading, privacy and security, and coordination. We conclude with future research directions, including learning-based mechanism design for satellite networks.

We investigate the minimum-delay multicast scheduling problem for mmWave networks by fully leveraging reflections. An analog beamforming system is considered, where both the orientation and beamwidth of the transmission beam can be adjusted to serve multiple receivers simultaneously. We formulate a combinatorial optimization problem that is difficult to solve directly. To address this challenge, we propose a polynomial-time algorithm that heuristically selects multicast groups based on the distances between receivers. Simulation results demonstrate that, despite lower complexity, the proposed algorithm achieves significant delay reduction compared to baseline approaches and performs close to optimal, with performance gains that increase with the number of reflected paths, showing good scalability.

Graph neural networks (GNNs) have been widely applied in software-defined network (SDN) to enhance network modeling and performance forecasting. However, the closed-box nature of deep learning makes GNNs difficult to interpret, hindering their broad use and the application of GNN-based SDN systems in engineering. In this paper, we propose a novel interpretation framework named GEEK-Explainer, designed to efficiently provide instance-level interpretation of GNNs in SDN. Specifically, we introduce a KernelSHAP-based scoring module to generate intuitive and human-friendly explanations for each performance prediction. To address conflicts in computation cost, we propose a soft discrete mask matrix that identifies a critical set of important nodes. Extensive experiments demonstrate that the RouteNet model can effectively learn the relationships among features, which can provide a better understanding of the prediction process with less computation cost. These findings improve the transparency and robustness of the model and promote the application of GNN-based SDN systems in engineering practice.

In this letter, we propose an accurate and efficient heterogeneous graph neural network-powered ray tracing (HGNN-RT) framework to predict path loss for urban scenarios. The proposed HGNN-RT effectively captures both global and local features, which include broad path attributes like total path length and line-of-sight (LoS) conditions, as well as specific interactions such as reflection angles and obstacles. Moreover, due to the fast information flow of the graph neural networks, the HGNN-RT achieves efficiency-improved path loss prediction in complex multi-reflection and multipath propagation channels. Experimental results demonstrate that the proposed approach achieves errors within 2 dB of RT benchmarks, while also being time-efficient and reliably generalizing to unseen environments for channel modeling.