Quan Chen

The evolution of non-terrestrial networks is culminating in the space computing power network (SCPN). This architecture integrates GEO satellites, LEO constellations, and ground clouds into a robust multi-layer computing fabric to support large-scale, delay-sensitive Internet of Things (IoT) services with global coverage. However, efficiently orchestrating IoT computation tasks necessitates addressing the simultaneous task offloading and resource allocation (STORA) problem, which is challenging due to cross-layer heterogeneity, time-varying resource availability, and the hybrid nature of discrete offloading decisions and continuous resource allocation. To address these challenges, we propose a GEO-LEO-Cloud cooperative SCPN framework and develop a hybrid-action multi-agent deep reinforcement learning (HA-MADRL) approach that decouples STORA into global task offloading and local resource allocation subproblems. Global task offloading is formulated as a partially observable Markov decision process and optimized via a customized multi-agent double deep Q-network (MADDQN), while local resource allocation is solved using the twin delayed deep deterministic policy gradient (TD3) algorithm. Extensive simulations under Starlink and OneWeb configurations validate that HA-MADRL consistently achieves lower task latency and energy consumption, significantly reduces task failure ratio, and improves load balancing among LEO satellites, thereby enabling scalable and reliable IoT task processing in SCPNs.

This paper focuses on the research of anti-jamming issues for Low Earth Orbit (LEO) satellite constellations. Initially, the anti-jamming problem is modeled as a Local Interaction Markov Game (LIMG) and proven to be an Exact Potential Game (EPG), with at least one pure strategy Nash Equilibrium (NE) existing. Secondly, based on the theoretical analysis and the ”offline training and online execution” architecture, a Distributed Multi-Agent Deep Reinforcement Learning-based anti-jamming (DMDRLA) scheme is proposed, and its convergence and asymptotic optimality are theoretically analyzed. Finally, simulations validate that the proposed DM-DRLA scheme can effectively balance the training costs and performance optimization of the anti-jamming model, making it suitable for anti-jamming issues in resource-constrained LEO satellite networks.

Mega-constellation networks (MCNs) face the significant threats of regional service denial attacks. To improve the robustness of regional services in MCNs against such attacks, a cost-effective approach is to identify key node sets for targeted protection efforts. This paper formally defines the key node set analysis problem for regional service denial in MCNs and develops a comprehensive solution framework. First, we develop a regional service capability analysis model that considers the dynamic collaboration of multiple satellites within regional communication service scenarios in MCNs, alongside a temporal network model for their collaborative relationships. Next, we design a multi-satellite criticality metric that quantifies the multi-dimensional impacts of satellite node set failures on regional service capabilities. Building on these, we construct a mixed-integer programming-based key node set analysis model to achieve precise identification of key node sets. Finally, simulation experiments are conducted to verify and analyze the proposed methods, providing insights to enhance the robustness of regional services in MCNs.

The escalating demand for global connectivity has driven Low Earth Orbit (LEO) constellation networks to become indispensable components of next-generation communication systems. As these networks evolve to support massive device connections, they generate unprecedented volumes of access network traffic requests, making accurate access network traffic prediction critically important. However, the online prediction of traffic presents significant challenges. These challenges stem from the lack of real-time correlation information, limited satellite resources, and the influence of sudden traffic changes. Due to the regular layout and predictable movement of the LEO constellation, the traffic of adjacent satellites within the same orbit plane has similar trends and strong correlations. Furthermore, the real-time traffic information can be shared between adjacent satellites via Inter-Satellite Links (ISL). Inspired by the above, this paper, for the first time, proposes an Adjacent Satellite Correlation-based Single-Hop Prediction (ASC-SHP) method for LEO constellation network online traffic prediction. This method explores the traffic correlations between adjacent satellites and employs neural networks to extract and renew the spatiotemporal deviation features of traffic between adjacent satellites autonomously. Simulation results validate that the proposed ASC SHP method reduces prediction errors by over 82% on average compared to baseline methods, with performance improving as the constellation size increases. Further simulations show that ASC-SHP remains effective under ISL instability and satellite failure conditions, while reducing computational complexity by over 80% compared to baseline methods.

Beam hopping (BH) is crucial for managing communication resources in large-scale multi-satellite, multi-beam Low Earth Orbit (LEO) constellation networks. However, achieving high-dynamic multi-satellite cooperative BH in LEO mega-constellations while satisfying multi-dimensional resource allocation demands remains a significant challenge. To address the interdependence of long-term coverage stability and short-term resource volatility, this paper proposes a distributed Beam Hierarchical Multi-Agent Reinforcement Learning (HMARL) framework. Within this framework, a hierarchical Duplex Dueling Multi-agent Q-learning (QPLEX) architecture decouples the high-dimensional joint optimization problem into two tractable layers: satellite-level cell association and beam-level resource scheduling. The former handles coverage planning under load-balancing and interference constraints, while the latter manages dynamic resource allocation amid spatiotemporal traffic variations. Additionally, Chemical Reaction Optimization (CRO) and Cross-Layer Attention (CLA) mechanisms are introduced to address the discrete beam interference problem and the inter-layer information isolation challenge, respectively. Simulation results show that the proposed method, BH-HMARL, performs better than baseline methods. Further results confirm its scalability, with performance remaining stable despite variations in the number of satellites, beams, and cells. Moreover, BH-HMARL achieves real-time scheduling with execution time under 5ms per agent, satisfying slot duration constraints.

A mega hybrid constellation comprising low Earth orbit (LEO) and geostationary orbit (GEO) satellites represents a prevalent architecture for future space-based networks. However, the emergence of mega constellations has exacerbated the shortage of spectrum resources. To address this issue, this paper investigates a method for LEO satellites within such constellations to expand their available spectrum by sharing the downlink spectrum of GEO satellites. Firstly, to avoid interference with GEO satellites and optimize the beam coverage for LEO user (LU), a coalition formation game model for LU based on cooperation criteria is constructed, and the existence of a stable coalition structure is proven. Secondly, to determine this stable coalition structure, a coalition formation game algorithm based on the best response (BR) is proposed, and its convergence is theoretically validated. Additionally, to more efficiently determine the beam radius and center covering the LU in the coalition, an improved algorithm for solving the outer circle of LU in the coalition using a K-dimensional tree is presented. Simulation results demonstrate that the proposed method effectively balances convergence time and accuracy. Without affecting GEO satellite communications, LEO satellites can share the downlink spectrum of GEO satellites, thereby enhancing the utilization of spectrum resources within the hybrid constellation.

As a crucial component of future space–terrestrial integrated networks, low Earth orbit (LEO) mega-constellation networks (MCNs) are facing increasingly prominent network congestion issues due to surging user demands, particularly around gateways with geographical constraints. To address this network congestion problem, this study proposes a user-to-gateway routing approach that implements a hybrid routing strategy for congestion mitigation (HRS-CM) under restricted gateway deployment conditions. The HRS-CM consists of three phases: an access satellite (AS) selection phase based on hop count evaluation models, a semi-distributed multihop congestion avoidance routing phase based on link costs, and a gateway-centered load-balancing routing phase. All three phases are modular, allowing for flexible combinations of partial phases. Compared to traditional methods, the proposed approach achieves significant reductions in communication delay and average hop count while maintaining high performance in terms of data delivery ratio and throughput. The simulation results demonstrate the superior performance of the proposed approach in scenarios with geographically constrained gateway deployment conditions.