In the Large-scale Satellite Network (LSN), inter-beam interference significantly hinders the transmission performance of Low Earth Orbit (LEO) satellite downlinks. This interference exhibits notable time-varying characteristics due to the relatively rapid movements between LEO satellites and ground users, posing a substantial challenge to existing transmission resource allocation techniques. To tackle this issue, we introduce the Hypergraph Neural Network (HGNN)-enabled Resource Allocation (HGNNRA) algorithm for the downlinks of LSN. This algorithm aims to solve a transmission-rate maximization problem, effectively handling the time-varying interference among multiple beams in the downlink through a well-tailored Dynamic Hypergraph Neural Network (DynHGNN). Specifically, considering the coupling complexity of satellite beam coverage and user participation, we have developed a Dynamic Hypergraph-based interference model, along with a customized construction algorithm, to describe their time-varying relationships precisely. Simulation results indicate that our proposed HGNNRA outperforms both Graph Convolutional Network (GCN) [14], HGNN [15], GNN-DDQN [48], and GCNRA in terms of transmission rate and the satisfaction degree of user transmission requirement metrics.

In the Large-Scale Heterogeneous Satellite Networks (LSHSNs) integrating Low Earth Orbit (LEO) and Medium Earth Orbit (MEO) satellites, data delivery faces complex topology and dynamic connectivity, which poses a significant challenge to current graph-dependable transmission strategies requiring global topological information, incurring huge computational cost and interactive overhead. To address this issue, in this paper, we propose a Local Information-dependable Semantic Delivery Mechanism (LISDM) by exploiting topological features at the semantic level, in which we develop a Task-oriented Semantic-aware Topology Compression Network (TSTCN) to condense the global topology according to specific task demands. Besides, we develop a Deep Q-Network enabled Semantic Coded Routing (DQNSCR) algorithm for the semantic delivery in the LISDM by designing a novel metric called Semantic Delivery Efficiency (SDE). The simulation results indicate that the proposed mechanism performs better in improving the required topology scale and throughput compared with typical data delivery mechanisms such as the conventional Open Shortest Path First (OSPF) routing algorithm, the DQN-based Intelligent Routing (DQN-IR) algorithm, and Real-Time Hop-by-Hop Routing (RTHop) algorithm with Space-Time Graph (STG) model, respectively.

The large-scale low-Earth orbit (LEO) satellite network presents an obvious challenge for user access with obviously dynamical coverage resulting from the fast-changing locations of LEO satellites, i.e., time-varying overlapped range in spatial and temporal coverage by multiple overhead satellites, making existing studies low throughput for supporting various users with fluctuating access demands. In this article, we propose a multiagent deep deterministic policy gradient-based access (MADDPGA) mechanism for the large-scale satellite network, which allows each user to adjust its strategy autonomously by learning the changing network conditions in the long term. By solving a throughput-maximizing optimization problem, we develop a fully decentralized multiagent deep reinforcement learning (MADRL) algorithm by exploiting optimal dormancy probability (DP) and a well-designed weighted allocation strategy. The simulation results show that the proposed method can effectively improve the throughput performance compared with the Random algorithm and fixed DP algorithm.

In Large-Scale Heterogeneous Satellite Networks (LSHSNs) integrating Low Earth Orbit (LEO) and Medium Earth Orbit (MEO) satellites, high-timeliness data delivery confronts dynamical connectivity and obvious latency, which heavily challenges existing graph-dependable transmission strategies requiring to obtain global topological information with huge computational cost and signaling overhead. To address this issue, in this paper, we propose an Age-predicting Local Information Dependable Transmission (ALIDT) mechanism for the LSHSN by considering the impact of time-varying topology on the timeliness of data, in which a novel metric of data freshness called Forwarding-aware Age of Information (FAoI) is well-designed to evaluate the timeliness in data forwarding at node. In particular, we develop a satellite Coverage-based Local Information Sharing (CLIS)-assisted Spatial-Temporal Graph Neural Network (STGNN) to extract the topological features in both temporal and spatial dimensions and a Graph Matching Network (GMN)-based topology compression algorithm to improve computation efficiency. The simulation results indicate that the proposed mechanism performs better in improving the storage overhead, throughput and average FAoI compared with the conventional Open Shortest Path First (OSPF) routing algorithm with Time-Varying Graph (TVG) model, GNN-based Multipath Routing (GMR) algorithm, and Gated Recurrent Units (GRU) based metric prediction algorithm in hybrid satellite networks, respectively.

In the Satellite-integrated Internet of Things (SIoT), typical multi-access schemes, i.e., Contention Resolution Diversity Slotted ALOHA scheme (CRDSA), face with the obvious challenge of heavily conflicting packets regarding high channel traffic, which is not well addressed by the methods of Successive Interference Cancellation (SIC) due to the stubborn loop issues. In this work, therefore, we develop a Generalized Deduplication (GD) based Contention Resolution Diversity Slotted ALOHA scheme with a Compulsory Divorce mechanism (CD-CRDSA) by considering the correlative properties among the accessing data from the users. In particular, the proposed mechanism could effectively separate individual packets from conflicting slots to maintain the sustainability of SIC process, thus achieve better throughput. Moreover, we make the theoretical analysis on the throughput performance with the compression gain of the proposed mechanism. The simulation results indicate that compared to the typical CRDSA protocol and the Non-Orthogonal Multiple Access (NOMA) scheme, the proposed CDCRDSA significantly reduces the amounts of un-resolved slots and improves throughput performance, especially in high-load areas.

Recently, the Satellite-Integrated Internet of Things (S-IoT) has attracted wide interest in remote data-gathering scenarios supporting requirements of user access within a wide area. Nevertheless, considering various priorities of timeliness, huge challenges in the diversified access scenario still exist, which could not be addressed efficiently by the current Irregular Repetition Slotted ALOHA (IRSA) access protocol. Therefore, in this paper, we propose an Value-Optimal Irregular Repetition Slotted ALOHA (V-IRSA) mechanism for the S-IoT via a distributed non-cooperative game theoretic approach, in which a utility function of the Cost of Information Value (CoIV) is developed for capturing the loss of information value during the packet transmission. By deriving the closed-form of the CoIV function, we make optimization on the proposed mechanism parameters during which various priorities of IoT devices could learn autonomously with their respective utilities in a selfish way. Numerical results show that the proposed mechanism could efficiently achieve a higher access probability for more emergent nodes in the system compared with the present algorithm.