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.

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.

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.

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.

With increasing global communication demands, Low Earth Orbit (LEO) mega-constellation networks have become critical for global connectivity. However, frequent satellite handovers due to limited coverage and dynamic network conditions challenge service continuity and load balancing. Traditional centralized approaches lack scalability, while distributed methods with fixed-weight metrics struggle in complex scenarios. To overcome these limitations, this paper proposes a Fuzzy Logic-based Distributed Satellite Handover (DSH-FL) algorithm. DSH-FL leverages fuzzy logic to dynamically assess satellite service quality using four key metrics and employs a lightweight redistribution mechanism to resolve conflicts and balance loads. Simulations demonstrate significant improvements in load balancing and fairness, while further simulations confirm its applicability across LEO mega-constellation networks of varying scales.

In 6G-oriented integrated space-air-ground networks, efficient routing in Low Earth Orbit (LEO) satellite constellations is critical. However, escalating demands for high-bandwidth terrestrial applications strain gateway station resources due to hardware and spectral limitations, while uneven user distribution exacerbates congestion at overloaded gateway stations. To address this, we propose a partitioning strategy that segments the ground into cells, clusters them into gateway-centered partitions to minimize link occupancy, and maps these partitions onto the mega-constellation network. This approach enhances global load balancing and reduces routing overhead. Within each partition, an improved Backpressure-Longer Side Priority (BP-LSP) routing algorithm further optimizes intra-partition traffic distribution. Simulations demonstrate that the proposed strategy significantly improves network load balancing, outperforming baseline methods in packet loss rate and throughput.

The traffic prediction method for Low Earth Orbit (LEO) satellite-based Internet of Things (IoT) provides prior conditions for addressing challenges in resource allocation and management of LEO satellite-based IoT. However, traditional methods rely on the pre-generation of terrestrial centers, leading to substantial computational complexity and delays when applying prediction results to LEO satellite-based IoT. Therefore, achieving autonomous LEO satellite-based IoT traffic prediction is essential. Unlike the spatiotemporal features of terrestrial IoT, LEO satellite-based IoT traffic is influenced by both satellite dynamics and terrestrial user behavior. Fortunately, real IoT traffic information can be shared between adjacent satellites through Inter-satellite Links (ISL). Inspired by the above, this paper proposes, for the first time, an autonomous LEO satellite-based IoT traffic prediction method. A Spatiotemporal Fusion Neural Network (SFNN) employing a four-layer hybrid neural network architecture is designed to accommodate the unique spatiotemporal features of LEO satellite-based IoT traffic. The simulation results demonstrate that the proposed method has low computational complexity and achieves a better prediction performance than the five baseline methods in LEO constellations. Further simulations demonstrate that the proposed method achieves higher accuracy in large-scale LEO constellations, and the algorithm remains applicable under ISL instability and satellite failure conditions.

A mega hybrid constellation of low-Earth orbit (LEO) and geosynchronous orbit (GEO) satellites represents a typical architecture for future space networks. However, the emergence of such large constellations exacerbates the shortage of spectrum resources. To address this issue, this article investigates a hierarchical optimization method for beam scheduling and power control of LEO satellites, aiming to extend the available spectrum by sharing the downlink spectrum of GEO satellites. In the upper layer optimization, a many-to-one matching game model is constructed to achieve optimal matching of LEO satellite beams and LEO users (LUs). A distributed beam matching learning algorithm (DBMLA) is designed to find a stable matching solution for the model, with the convergence of the algorithm theoretically proven. In the lower layer optimization, an LEO beam power level optimization game model is developed for discrete LEO beam power levels. This model is demonstrated to be an exact potential game with at least one Nash equilibrium (NE). To solve this NE, a dynamic power allocation logarithmic learning algorithm (DPALLA) is proposed. Simulation results verify that the proposed DBMLA-DPALLA hierarchical optimization scheme effectively balances convergence time and accuracy compared to traditional independent optimization strategies. By leveraging the combined effects of the two optimization strategies, it better mitigates the co-channel interference experienced by GEO satellites and improves the average network satisfaction of the LUs.

As a crucial component of future space-terrestrial integrated networks, Low Earth Orbit (LEO) mega-constellation networks 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 selection phase based on hop count evaluation models, a semi-distributed multi-hop 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.

The Satellite-based Internet of Things (S-IoT) system offers a promising solution for accessing IoT devices in areas where terrestrial networks are inaccessible. These devices have significant roles in various scenarios, including aeronautic and maritime surveillance, data collection from sensors, and distress signals, from sinking ships. However, covering numerous IoT targets with a minimal number of satellites poses a challenge. This article proposes a solution known as the 5S payload designed for S-IoT. The 5S payload integrates five subsystems into a single unit, comprising the following components: 1) automatic identification system (AIS) for ship surveillance; 2) very high-frequency data exchange system (VDES) enabling two-way communication for ships; 3) automatic dependent surveillance-broadcast (ADS-B) system for aircraft surveillance; 4) data collection system (DCS) for user-defined sensors; and 5) emergency search and rescue (ESR) system. The design of the 5S payload adheres to the CubeSat standard and occupies a compact 1.5U size, enabling rapid manufacturing and deployment. The feasibility of the 5S payload was demonstrated on the TianTuo-5 satellite launched on 23 August 2020. Additionally, this article presents in-orbit experimental results spanning three years to further illustrate the effectiveness of the 5S payload.

The Shortest Distance Path (SDP) problem is a critical routing issue in communication networks, particularly in satellite networks. Typically, SDP is solved by graph-based iterative algorithms, while an explicit or analytic approach is challenging. However, considering the orbit dynamics and topology regularity, this paper proposes, for the first time, an explicit analytic phase-based algorithm STEPCLIMB to directly solve the SDP in low-Earth orbit (LEO) satellite networks. Based on the relationship between satellite phase and inter-satellite link distance, the SDP is modeled with the satellite phase, and SDP problem is converted into a total phase offset problem through theoretical derivations. Then STEPCLIMB is derived in two cases, respectively. Monte-Carlo simulations verify STEPCLIMB’s accuracy, which has zero error in the mono-valley case and has less than 0.1% error in the bi-valley case. The algorithm performs better in larger-scale constellations and can save over 99.4% computational cost compared to Dijkstra algorithm. Further, the SDP pattern and features in Starlink constellation are analyzed. The model proves that most inter-plane hops in the SDP occur successively, and the simulations further indicate that these hops prefer satellites in the higher latitude regions.

Large-scale satellite constellations lead to a scarcity of spatial spectrum resources, especially for the overlapped spectrum between Low Earth Orbit (LEO) and Geostationary Orbit (GEO)satellites in hybrid constellations. Hence, based on game theory, a distributed spectrum-sharing method is proposed for downlink spectrum sharing in large-scale hybrid satellite constellations. Specifically, the system cycle is divided into equal-spaced topological periods, and the beams ofLEOsatellites are assigned toLEOground stations (LGS) during every topological period. A system model based on game theory is also developed to describe the mutual interference of links established betweenLEOsatellites andLGS. Subsequently, the formulated game is proven to be an exact potential game, with at least one pure strategic Nash equilibrium (NE). Along the line, to obtain the NE solution, a dynamic channel allocation algorithm is proposed based on stochastic learning theory, and the convergence is proven. Finally, the simulation results demonstrate the proposed DCASLA’s effectiveness, which can balance the convergence speed and overall network satisfaction.

Megaconstellation networks, represented by Starlink and OneWeb, have become a promising solution for the wide-area Internet of Things (IoT). IoT messages collected by the satellite can be routed to the ground gateway via multiple intersatellite link (ISL) relays and then access the ground network. Compared to traditional constellations, megaconstellations with massive satellites require more ISL relays that are greatly affected by the number and location of ground gateways. In this article, we focus on the ISL usage for connecting satellites and gateways and propose a novel method with low computation cost to evaluate the ISL usage metric. Then, we formulate a mixed-integer optimization model for the gateway site optimization (GSO) problem with minimizing the overall ISL usage, which is then simplified through model transformation. An IBD-PSO algorithm is proposed to solve the transformed GSO problem. Based on the Starlink constellation, simulation results have verified the proposed method by comparisons with previous studies. We further investigate how the gateway placement is affected by the gateway number and traffic demand pattern. The relations between gateway placement and ISL hop count of different satellites are also studied.

Software-defined networking (SDN) logically separates the control and data planes, thus opening the way to more flexible configurations and management of low-Earth orbit (LEO) satellite networks. Since one or, more generally, multiple distributed controllers are needed, a significant challenge in SDN is the controller placement problem (CPP). Due to characteristics such as the dynamic network topology, limited bandwidth and traffic variations, the CPP is quite complex in SDN-based satellite networks. In this paper, we propose solving the CPP by means of a static placement with dynamic assignment (SPDA) method for LEO satellite networks. The SPDA method has two parts: the first is to incorporate SDN controllers into some fixed satellites by formulating a mixed integer programming model; the second is to dynamically assign switches to existing controllers according to the switch-controller latency and the traffic load of controllers. The SPDA method takes the topological dynamics into account by effectively dividing time snapshots, and it has a lower bandwidth consumption compared with methods involving controller migrations. Real satellite constellations are used to evaluate the performance of our controller placement solution. The results show that SPDA outperforms existing methods in terms of reducing the switch-controller latency, and it also has good load balancing performance.

Software-Defined Satellite Networking (SDSN) has emerged as a new paradigm that offers the programmability required to flexibly manage a satellite network. A key concern in the deployment of SDSN is the controller placement, i.e. how to find the optimal number and location of controller(s). So far, some articles have studied this problem, but they do not study how to update controller placement at minimum cost when expanding a satellite network. This paper introduces an update method for the controller placement problem in SDSN. Given the existing topology of a satellite network and the list of switches to be added to the network, the update method will find how to relocate the controller(s) such that the update cost is minimized.