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 computing power network (CPN) offers exceptional computational capabilities and reliable network services, with significant potential for future applications. To achieve ubiquitous coverage and efficient computational resource allocation, CPN can be seamlessly coordinated with low-cost autonomous aerial vehicle (AAV)-based mobile computing platforms. This article investigates an efficient low-altitude AAV-assisted computing power and resource allocation mechanism tailored for urban environments. The aim is to ensure seamless scheduling and efficient processing of computational tasks across various computing devices at different layers of the CPN, while minimizing AAV energy consumption and ensuring flight safety. First, this article proposes an Urban AAV-assisted CPN task-allocation and AAV trajectory-management decision-making problem. The AAV works until it safely lands, aiming to minimize overall task processing delay and AAV energy consumption while ensuring fairness in task allocation. Then, a novel AAV-protection-based multiagent deep deterministic policy gradient (UP-MADDPG) algorithm is introduced. It offers dynamic management of secure computing and communication flight paths when facing building blockages. Finally, we compared the proposed algorithm with baseline algorithms across various metrics. Experimental results demonstrate that the proposed algorithm achieves lower and more balanced task execution delay and AAV energy consumption while also improving fairness.

In the contemporary landscape of computationally intensive applications, Computing Power Network (CPN) offers a solution to enhance computational efficiency and cost-effectiveness by integrating and sharing computing resources. However, with the surge in task volume within multi-user environments, effectively scheduling these tasks to optimize system profit and delay presents a significant challenge. This paper introduces an optimization approach leveraging Deep Deterministic Policy Gradient (DDPG) to enhance CPN performance through task decomposition and computing path optimization. We initially construct a multi-layer CPN system model encompassing cloud computing, edge computing, and terminal device layers. Subsequently, we integrate a novel mechanism for convex optimization-based task decomposition, enabling intelligent subdivision of tasks into sub-tasks and dynamic allocation to suitable nodes within the network. Furthermore, we devise a Convex Optimization Task Decomposition-based Multi-Agent Deep Deterministic Policy Gradient (CO-MADDPG) algorithm, empowering multiple computing tasks as independent agents to learn and identify optimal offloading paths and computing nodes, thereby minimizing delay and maximizing system profit. A series of simulation experiments validate the effectiveness of the CO-MADDPG algorithm in handling concurrent tasks, demonstrating its capability to reduce task completion times, enhance system revenue, and maintain adaptability and stability across varying task demands.

We address the minimum-delay beam scheduling problem leveraging reflections for switched beamforming systems. The objective is to efficiently disseminate a data file from a transmitter to a set of nodes via multiple predetermined beams with arbitrary overlapping patterns. The problem is formulated as a challenging mixed integer nonlinear programming (MINLP) and then decomposed into a set of subproblems. The subproblems are still difficult to solve due to their NP-hardness. We propose a heuristic algorithm for the subproblems, based on which two heuristic algorithms with different computational complexities are developed for the original problem. Simulation results high-light the significant reduction in dissemination delay achieved by the proposed algorithms compared to baseline approaches without leveraging reflections.

In this article, a time-efficient mechanism is proposed to solve the joint unmanned aerial vehicle (UAV) base station deployment and user/sensor association (UDUA) problem aiming at maximizing the downlink sum transmission throughput and reducing the time of online computations. In this work, two relevant subproblems are decoupled from the joint UDUA problem: the first one is denoted as the user association subproblem, for certain UAV base station (UAV-BS) positions, this subproblem is settled for finding the strategy which matches aerial and ground nodes optimally. The second subproblem is the positioning of UAV-BSs in order to obtain the best possible solution to the user association subproblem from all possible positioning combinations for the UAV-BSs. In the proposed mechanism, the Kuhn–Munkres algorithm is used to solve the first subproblem as an equivalent bipartite matching problem. For the UAV-BS deployment subproblem, when the two user distributions own a high similarity, we theoretically prove that little performance decline will be introduced when the new user distribution’s optimal strategy is compared with choosing the optimal UAV-BS deployment strategy of stored user distributions. Based on our mathematical analyses, the similarity level between user distributions is well defined and becomes the key to solve the second subproblem. According to experimental findings, the proposed UDUA mechanism, when compared to benchmark approaches, can provide near-optimum system performance with regard to average downlink total transmission throughput and failure rate with significantly decreased computing time.

Modelling cellular traffic pattern plays a critical role to efficiently satisfy consumers’ demand, which is skewed distributed and fast-varying. Although current methods can indicate the traffic fluctuation of each cell, it is still not enough as optimisation techniques require to know the traffic distribution inside cells. In this paper, Neural Network is used to improve the resolution to intra-cell level by modelling the hotspots of geo-tagged Twitter. This paper is based on our previous work, which has already quantified the linear relationship between Tweets and mobile traffic. Here, a similarity measurement is designed to quantify how two patterns are similar to each other. We applied this measurement on the geo-tagged Tweets and found that in one of three periods (day, evening, and night), the hotspots distributions are more similar than the other periods. Then for each period, Generative Adversarial Networks (GAN) is used to train a generator for modelling the intra-cell hotspots distribution. Such a trained generator can also continuously generate convincing artificial-data to expand the data set. The similarity measurement gives high similarity (above 0.8) between generated artificial-data and the real test data.