Search Results (51)

Low and Medium Earth Orbit (LEO/MEO) satellite constellations have emerged as a compelling architectural paradigm for delivering multipurpose services. In this study, we investigate the joint optimization of communication and positioning performance metrics in a multilayer LEO/MEO constellation equipped with inter-satellite links (ISLs). A genetic algorithm framework is employed to optimize key constellation design variables, including the number of satellites per orbital plane, the number of planes, and the inter-plane phase offsets across layers, to minimize end-to-end user latency and Geometric Dilution of Precision (GDOP), subject to coverage and total satellite count constraints. Numerical results highlight the trade-offs among different architectural options. Full article

Low Earth Orbit (LEO) mega-constellations are becoming the backbone of global communications, yet their cybersecurity remains critically under-addressed. Intrusion detection systems (IDSs) for such constellations face a unique trilemma of accuracy, efficiency, and interpretability under extreme SWaP-C (size, weight, power, and cost) constraints. We present FedLTN-CubeSat (FedLTN refers to Federated Logic Tensor Networks), a neuro-symbolic federated learning framework for intrusion detection in LEO CubeSat constellations. The framework first employs a lightweight spatio-temporal separable perception encoder to efficiently extract features from telemetry and IQ data, designed to operate within the computational budgets of resource-constrained on-board processors. These features feed into a differentiable first-order logic layer based on Logic Tensor Networks, which incorporates domain knowledge as logical axioms to guide learning and enhance interpretability. To enable collaborative learning across a constellation, FedLTN-CubeSat introduces an intra-orbit symbolic federated learning mechanism that aggregates only the logic-layer parameters via inter-satellite links, drastically reducing communication overhead while preserving data privacy. Furthermore, an orbit-adaptive predicate migration module transfers learned rules across different orbital configurations with minimal supervision, facilitating rapid deployment. We evaluate on two benchmarks: the CuCD-ID dataset (NASA NOS3 telemetry) and the STIN dataset (satellite-terrestrial integrated networks). FedLTN-CubeSat achieves 0.98 F1-score on CuCD-ID and 0.96 accuracy on STIN—significantly outperforming prior federated learning baselines (7% improvement) while incurring a minimal daily communication load per satellite. The framework also outputs interpretable decision traces grounded in logical axioms, enabling operators to understand and validate detections. Logical constraints improve detection of unseen attack variants by 25% over pure neural baselines. Full article

With the development of 6G communication technology, Space–Air–Ground Integrated Networks (SAGINs) have become critical infrastructure for global intelligent collaborative computing. However, federated learning deployment in SAGINs faces three severe challenges: the high dynamics of satellite orbital motion, node resource heterogeneity, and privacy vulnerabilities in data transmission. This paper proposes an adaptive compressed sensing differential privacy federated learning framework based on orbital spatiotemporal characteristics. First, we design orbital periodicity-driven time-varying sparse sensing matrices that dynamically adjust compression strategies according to satellite orbital positions, achieving intelligent communication efficiency optimization. Second, we propose an orbital predictability-based privacy budget temporal allocation mechanism and perform differential privacy noise injection in the compressed domain, establishing a compression–privacy joint optimization algorithm. Furthermore, we construct an energy–communication–privacy ternary collaborative mechanism that achieves multi-objective dynamic balance through model predictive control. Finally, we design reinforcement learning-based dynamic routing scheduling and hierarchical aggregation strategies to effectively handle the time-varying characteristics of network topology. Simulation experiments demonstrate that compared to existing methods, the proposed approach achieves 3–12% improvement in model accuracy and 30–50% enhancement in communication efficiency while maintaining differential privacy protection with dynamic privacy budget $\epsilon \in [0.1,10.0]$ and compression ratio $\rho \in [0.2,0.8]$. Unlike static compressed sensing approaches that ignore orbital periodicity, the proposed orbital-driven time-varying sensing matrices reduce reconstruction error by up to 19.4% compared to fixed-matrix baselines, validating the synergistic effectiveness of integrating orbital spatiotemporal characteristics with federated learning in 6G SAGIN deployments. The framework assumes reliable orbital propagation via SGP4/SDP4 models and does not account for Doppler frequency shifts or inter-satellite link handover delays; future extensions include scalability to mega-constellations and integration of quantum-resistant privacy mechanisms. Full article

The accumulation of space debris in near-Earth orbit, particularly in Low Earth Orbit (LEO), poses an increasing threat to satellite operations, communication infrastructures, and long-term space sustainability. As modern constellations expand and incorporate advanced satellite technologies, including sensing and wireless communications, artificial intelligence-of-things (AIoT), enabled payloads, and edge computing for on-orbit data processing, the risk profile grows. This paper reviews the current debris environment and existing sensing and monitoring techniques, highlights major collision events and deliberate debris-generating activities, and analyzes the role of both governmental and commercial satellite constellations in exacerbating and mitigating the challenges. Emerging space surveillance and tracking (SST) techniques, leveraging radar, optical sensors, and interferometric SAR for enhanced intelligence, surveillance, and reconnaissance (ISR), are highlighted alongside software-defined networking (SDN) approaches and cloud communication technology that enable coordinated debris-avoidance maneuvers. Key international regulatory frameworks, tracking architectures, and mitigation measures, including alignment with ISO 24113 standards, advanced TT&C capabilities, and evolving active debris removal technologies, are examined. The study emphasizes the necessity of a global, interoperable ecosystem that integrates AI/ML (artificial intelligence and machine learning)-driven situational awareness, secure SATCOM links with AJ/LPI/LPD (anti-jamming/low probability of interception/low probability of detection) characteristics, and collaborative protocols among space agencies, commercial operators, and regulatory bodies to ensure the sustainable use of orbital space for future generations. Full article

The deployment of low Earth orbit (LEO) satellite mega-constellations enables global broadband access, but their high orbital velocity demands frequent handover decisions that critically impact service continuity. Conventional strategies that maximize instantaneous signal quality often trigger excessive handovers, while stability-focused approaches may sacrifice link performance. In this paper, we propose the Hybrid Handover Strategy (HHS), a low-complexity algorithm that addresses this trade-off. The HHS utilizes a multi-attribute utility function that integrates the signal-to-interference-plus-noise ratio (SINR), satellite elevation angle, and network load with a novel logistic-decay stability bonus mechanism. We provide a formal mathematical analysis of the algorithm’s stability and performance trade-offs. To ensure industrial relevance, the strategy is validated using a high-fidelity simulator driven by real-world two-line element (TLE) data from the Starlink constellation. Results demonstrate that the HHS reduces the handover frequency by 64% compared to SINR-based benchmarks while maintaining service availability of 90.2%. The proposed algorithm delivers these improvements with significantly smaller computational overhead than machine learning approaches, making it suitable for resource-constrained on-board processing and ground terminals. Full article

With the deployment of mega-constellations, the proliferation of on-orbit Resident Space Objects (RSOs) poses a severe challenge to Space Situational Awareness (SSA). RSOs produce elongated and stripe-like signatures in long-exposure imagery as a result of their relative orbital motion. The accurate detection of these signatures is essential for critical applications like satellite navigation and space debris monitoring. However, on-orbit detection faces two challenges: the obscuration of dim RSOs by complex stray light interference, and their dense overlapping trajectories. To address these challenges, we propose the Shape-Aware Attention Network (SAANet), establishing a unified Shape-Aware Paradigm. The network features a streamlined Shape-Aware Feature Pyramid Network (SA-FPN) with structurally integrated Two-way Orthogonal Attention (TTOA) to explicitly model linear topologies, preserving dim signals under intense stray light conditions. Concurrently, we propose an Adaptive Linear Oriented Bounding Box (AL-OBB) detection head that leverages a Joint Geometric Constraint Mechanism to resolve the ambiguity of regressing targets amid dense, overlapping trajectories. Experiments on the AstroStripeSet and StarTrails datasets demonstrate that SAANet achieves state-of-the-art (SOTA) performance, achieving Recalls of 0.930 and 0.850, and Average Precisions (APs) of 0.864 and 0.815, respectively. Full article

Large-scale Low Earth Orbit (LEO) satellite constellations have become critical infrastructure for global communications, yet routing optimization remains challenging. Due to high-speed satellite mobility and limited local perception capabilities, traditional shortest-path algorithms struggle to adapt to dynamic topology changes and effectively handle random fluctuations in traffic loads and inter-satellite link states. Meanwhile, as constellation scales expand, centralized routing mechanisms face deployment difficulties due to high communication latency and computational complexity. To address these issues, this paper proposes a hierarchical load-balanced routing optimization algorithm based on geographic partitioning. The algorithm divides the constellation into multiple regions by latitude and longitude, establishing a hierarchical cooperative decision mechanism: the upper layer handles inter-region routing decisions while the lower layer manages intra-region routing optimization. Within regions, a load-aware K-shortest paths algorithm enables path diversification, achieving global coordination through cross-region information sharing and dynamic path selection, thereby reducing end-to-end routing latency while enhancing adaptability to dynamic environments and balancing routing performance with system scalability. In simulation scenarios with a Starlink-like architecture of 1512 satellites, experimental results demonstrate that compared to shortest-path routing, the algorithm reduces end-to-end latency by 14.1% and average satellite load by 15.9%. Under dynamic load scenarios with incrementally increasing user traffic, the algorithm maintains stable performance, validating its robustness under traffic fluctuations and link state variations. Full article

This paper explores the economic, environmental, and security issues created by the launch of satellite megaconstellations, which are networks of LEO (Low Earth Orbit) satellites planned to provide worldwide communications, data services, and research capabilities. Although such programs bring the potential to offer global coverage and substantial technology enhancements, they also pose significant challenges to fund and sustain. In order to address these issues, the approach assumes a Life Cycle Costing (LCC) scope that includes development, launch, operational, end-of-life, and environmental impacts. Based on this, we introduce an original model, which includes a Cooperative Game Theory component—more precisely the Shapley value—to devise fair and efficient cost-sharing mechanisms between multiple players. The model includes the effects of cooperation, free-rider phenomena, and the consideration of capacity limitations, providing a formalized approach to distribute costs fairly and ensure coalition stability. A three-operators case study demonstrates the real benefits achieved by collaboration: significant cost savings of up to 27% compared with independent approaches. However, the analysis also demonstrates the destabilizing effects of free riders, which undermine cooperation in the short run and may lead to a net increase in costs for contributing parties. The results indicate that resilient allocation mechanisms and policy protection are necessary to secure the sustainability of megaconstellations over the long time period, possibly also applicable to other critical infrastructures beyond space systems. Full article

To support the 6G vision of seamless “space–air–ground-integrated” global coverage, optical satellite networks must enable high-speed, low-latency, and intelligent data transmission. However, conventional inter-satellite laser link-based optical transport networks suffer from inefficient bandwidth utilization and nonlinear latency accumulation caused by multi-hop routing, which severely limits their ability to support ultra-low-latency and real-time applications. To address the critical challenges of high topological complexity and stringent real-time requirements in satellite elastic optical networks, we propose an asynchronous advantage actor–critic-based quality of service-aware routing optimization mechanism for the optical inter-satellite link (OISL-AQROM). By establishing a quantitative model that correlates the optical service unit (OSU) C value with node hop count, the algorithm enhances the performance of latency-sensitive services in dynamic satellite environments. Simulation results conducted on a Walker-type low Earth orbit (LEO) constellation comprising 1152 satellites demonstrate that OISL-AQROM reduces end-to-end latency by 76.3% to 37.6% compared to the traditional heuristic multi-constrained shortest path first (MCSPF) algorithm, while supporting fine-grained dynamic bandwidth adjustment down to a minimum granularity of 2.6 Mbps. Furthermore, OISL-AQROM exhibits strong convergence and robust stability across diverse traffic loads, consistently outperforming MCSPF and deep deterministic policy gradient (DDPG) algorithm in overall efficiency, load adaptability, and operational reliability. The proposed algorithm significantly improves service quality and transmission efficiency in commercial mega-constellation optical satellite networks, demonstrating engineering applicability and potential for practical deployment in future 6G infrastructure. Full article

The design of mega-constellations poses a formidable challenge, as the selection of an optimal configuration directly governs system-level performance, while the computational efficiency of the design methodology remains a critical concern. To address this, this paper presents a high-efficiency, versatile optimization framework predicated on a genetic algorithm. The framework is architected to design diverse configurations, including Walker-$\delta$ and Rose constellations, and supports two distinct optimization objectives: the minimization of satellite count for prescribed performance requirements, or the maximization of coverage performance for a fixed number of satellites. To ensure computational tractability, the GA is holistically integrated with a rapid and accurate coverage analysis engine based on an area-adaptive uniform point distribution. The framework’s efficacy and validity are rigorously demonstrated through extensive simulations. The results exhibit strong consistency with the industry-standard Systems Tool Kit 11 software, with average deviations for key performance indicators—namely, coverage time ratio, average coverage multiplicity, and revisit time—controlled within 1%, 0.1, and 35 s, respectively. Moreover, when applied to a specific optimization task, the algorithm successfully identified a 181-satellite constellation that satisfied a given revisit requirement. The proposed method therefore constitutes an efficient, reliable, and automated tool for the design of complex mega-constellation architectures, promoting the diversified development of constellation configurations and enhancing the performance and resource optimization of satellite systems. Full article

Grid point approaches for LEO mega-constellation coverage analysis become computationally prohibitive for constellations exceeding 10³ satellites due to exponential complexity growth. This paper presents the Mercator projection superposition (MPS) approach, which transforms coverage evaluation into a two-dimensional image-processing paradigm by projecting the satellite coverage onto Mercator maps. MPS decouples computational complexity from satellite count, enabling analysis of constellations exceeding 10⁴ satellites. Validation against grid point approaches shows ≤1.2% error with 60× speed improvement for 103-scale constellations without parallel computation. The method establishes that instantaneous coverage rates reliably approximate periodic rates within 0.04% precision for early-stage constellation design. Analysis of Starlink-based configurations identifies optimal principles governing mega-constellation coverage, with particular emphasis on configuration and orbital parameter relationships. These findings enable rapid design iteration and optimization for future mega-constellation development. Full article

With the maturation of reusable launch vehicle technology and satellite mass-production capabilities, global mega-constellation projects have entered a phase of rapid expansion. Inter-satellite networking is a key approach for enhancing constellation performance, as it crucially impacts overall constellation effectiveness. However, existing studies mostly focus on the network layer protocol optimization, with insufficient attention to topological structure design, and fail to fully consider the engineering challenges associated with inter-orbit Inter-Satellite Links (ISLs). To address these issues, this paper proposes a heterogeneous ISL topology architecture for mega-constellations, centered on “stable high-speed laser backbone connection within intra-orbit planes + dynamic and flexible radio network between inter-orbit planes”. First, we clarify the optimization objectives for mega-constellation topological design under this architecture and theoretically prove that the optimization problem is NP-hard. Building on this, we introduce Topological Structure Units (TSUs) and employ a unit reuse strategy to simplify topological design. Furthermore, we propose a TSU-based heterogeneous ISL topological design algorithm. Considering the uneven satellite distribution across latitude zones within the constellation, we further propose a regional TSU-based topological design algorithm. Finally, through simulation experiments in Starlink and GW constellation scenarios, we conduct multi-dimensional verification to demonstrate the effectiveness of the proposed algorithms in reducing end-to-end delay and decreasing ISL hops. Full article

The evolution of 3GPP non-terrestrial networks (NTNs) is enabling new avenues for broadband connectivity via satellite, especially within the scope of 5G. The parallel rise in satellite mega-constellations has further fueled efforts toward ubiquitous global Internet access. This convergence has fostered collaboration between mobile network operators and satellite providers, allowing the former to leverage mature space infrastructure and the latter to integrate with terrestrial mobile standards. However, integrating these technologies presents significant architectural challenges. This study investigates 5G NTN architectures using satellite mega-constellations, focusing on transparent architectures where Starlink is employed to relay the backhaul, midhaul, and new radio (NR) links. The performance of these architectures is assessed through a testbed utilizing OpenAirInterface (OAI) and Open5GS, which collects key user-experience metrics such as round-trip time (RTT) and jitter when pinging the User Plane Function (UPF) in the 5G core (5GC). Results show that backhaul and midhaul relays maintain delays of 50–60 ms, while NR relays incur delays exceeding one second due to traffic overload introduced by the RFSimulator tool, which is indispensable to transmit the NR signal over Starlink. These findings suggest that while transparent architectures provide valuable insights and utility, regenerative architectures are essential for addressing current time issues and fully realizing the capabilities of space-based broadband services. Full article

This paper explores the usage of LEO-PNT (Positioning, Navigation, and Timing) for providing navigation integrity to GNSS (Global Navigation Satellite System) constellations. LEO mega-constellations, which are positioned between GNSSs and users, offer closer-to-the user geometry, improving performance, reducing the time to alarm (TTA) and enabling integrity monitoring without complex ground segments of any sort. The aim is to use future LEO mega-constellations as integrity monitors for a forthcoming European Global Navigation Satellite System (EGNSS) specifically focused on automotive users, which has minimal onboard satellite capabilities and no ground involvement. This plan builds on earlier studies, anticipating the performance of the upcoming LEO-PNT In-Orbit Demonstration. Full article

The development of low-orbit satellite communication networks marks the beginning of a new era in global communication. However, in the context of large-scale LEO satellite communication scenarios, the traditional adjacent connection transmission method limits the advantages of low latency in optical communication. Multi-hop transmission increases the number of hops and propagation distance, thereby affecting time-sensitive business transmissions. Therefore, based on the design of optical interconnect parallel subnetworks, this paper proposes a scheduling strategy for time-sensitive business transmissions between LEO satellites. Firstly, this strategy integrates the gate control scheduling mechanism from Time-Sensitive Networking (TSN) transmission in the interconnect parallel subnetwork scenario. Secondly, considering issues like queuing after subnetwork division, excessive burden, and algorithm complexity, mathematical problem abstraction modeling is applied to subsequent route scheduling, with reinforcement learning used to solve the problem. Through simulation experiments, it has been observed that compared to SPF (Shortest Path First) and ELB (Equal Load Balance), this approach can effectively enhance the control capability of end-to-end latency for TSN services in long-distance transmissions within Low Earth Orbit mega-constellations. The integration of reinforcement learning decision algorithms also reduces the complexity compared to traditional constraint-solving algorithms, ensuring a certain level of practicality. Overall, this solution can enhance the communication efficiency and performance of time-sensitive services between satellite constellations. By integrating time-sensitive network transmission technologies into optically interconnected subnets, further exploration and realization of low-latency and controllable latency satellite communication networks can be pursued. Full article