Search Results (812)

As 6G mobile communications evolve, Low Earth Orbit (LEO) satellite mobile edge computing (MEC) enables globally seamless computing. However, the high mobility of LEO satellites disrupts service continuity and resource stability. Existing approaches often use oversimplified models that ignore multi-beam interference and dynamic task queueing. To address this, we establish a hierarchical Geostationary Earth Orbit (GEO)–LEO synergistic architecture, where the integration is implemented by utilizing GEO satellites as stability anchors and remote cloud relays, while LEO satellites provide low-latency edge processing. We formulate fine-grained models for two-level beam-centric communication and preemptive dynamic queueing. The resulting joint task offloading and resource allocation problem is a complex mixed-integer nonlinear program (MINLP). To effectively solve this MINLP, we decouple it hierarchically: first determine discrete offloading decisions, then optimize continuous resource allocations based on them, proposing a novel framework termed G²DRL (GNN-enhanced Game-theoretic and deep reinforcement learning). Simulation results demonstrate that G²DRL significantly reduces the weighted sum of system delay and energy, showing superior convergence stability and performance over state-of-the-art DRL baselines. Full article

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

In Low Earth Orbit Beam-Hopping Satellite Systems (L-BHSS), co-channel interference among beams severely degrades communication quality. To address the inter-beam co-channel interference avoidance problem, this paper proposes a Parameter-Sharing Multi-Agent Deep Deterministic Policy Gradient-Based Beam Geometry Multi-Parameter Optimization Algorithm (PS-MADDPG-BGMPOA) for the joint optimization of satellite beam geometric parameters. The effects of free-space path loss, atmospheric impairments, and Rician fading are comprehensively considered, and a beam geometric multi-parameter optimization model is formulated with the objective of maximizing the long-term Signal-to-Interference-plus-Noise Ratio (SINR), incorporating beamwidth, beam center offset from the satellite nadir direction angle, inter-beam separation angle, and beam activation states. To tackle the resulting high-dimensional mixed action space, the proposed algorithm employs parameter sharing and grouped decision-making, which alleviates the dimensionality explosion problem and decouples the network scale from the number of beams, enabling efficient cooperative optimization with reduced training complexity. Simulation results show that, under various channel conditions and beam configurations, the proposed method effectively enhances communication quality and spectral efficiency while exhibiting superior real-time performance and stability. Full article

The Galileo High Accuracy Service (HAS) offers an opportunity to enhance the onboard real-time precise orbit determination (POD) and navigation payload design for low-Earth-orbit (LEO) and low-medium-Earth-orbit (MEO) satellites. Spaceopal, in collaboration with the German Aerospace Center, is developing a novel Galileo HAS receiver tailored for real-time POD and LEO/low MEO navigation payloads. This receiver provides autonomous onboard knowledge of the satellite’s orbit with centimeter accuracy in real time, enabling cost-efficient operations, better collision prediction, and accurate payload pointing among other benefits. Additionally, the POD receiver facilitates time synchronization for LEO/low MEO PNT navigation payloads. Full article

Physical-layer key generation (PLKG) is a technique that produces symmetric encryption keys by exploiting the inherent characteristics of wireless channels. It offers advantages including high physical-layer security, elimination of pre-shared keys, dynamic upgradability, and resistance to quantum attacks, making PLKG a promising security solution for next-generation (6G) networks. However, satellite communication channels exhibit high dynamics and long propagation delays. Characteristics such as large Doppler shifts, short coherence times, and orbital predictability pose severe challenges to PLKG, including reciprocity degradation, low key generation rate (KGR), and susceptibility to channel-prediction attacks. This work proposes a delay-Doppler domain time-hopping key generation scheme (KE-DD-TH) based on Orthogonal Time Frequency Space (OTFS) modulation for high-speed links between Low-Earth-Orbit (LEO)/Medium-Earth-Orbit (MEO) satellites and ground terminals in Ka/Ku bands. The scheme performs non-uniform sampling on the DD domain grid of OTFS symbols using an ephemeris-driven pseudo-random time-hopping sequence generated by cascaded linear feedback shift registers (LFSRs) and a nonlinear matrix transformation. Both legitimate parties estimate the channel only at time-hopping instants and multiply two adjacent estimates to construct an “equivalent channel” matrix, yielding a random source with high entropy, high reciprocity, and low predictability. The eavesdropper’s key disagreement rate (KDR) remains close to 0.5 under all signal-to-noise ratio (SNR) conditions, corresponding to the ideal random-guessing baseline. This indicates that Eve obtains negligible mutual information, i.e., $I(K_A;K_E)\approx 0$. By contrast, the conventional KE-DD scheme allows Eve’s KDR to degrade to 0.014 at 30 dB SNR, indicating near-complete key recovery. The generated keys pass all 12 randomness tests of the NIST SP 800-22 statistical test suite. Full article

The growing demand for accurate and timely Earth observation (EO) data has made autonomous mission planning increasingly essential. In particular, data acquisition planning has gained attention in recent years with the advent of agile Earth observation satellites (AEOSs). This process involves two main stages: target identification and observation scheduling. Traditionally, the former is performed manually, while the latter requires solving the agile Earth observation satellite scheduling problem (AEOSSP), a complex combinatorial optimization problem. In this work, we propose a constellation design comprising EO satellites deployed in medium Earth orbit (MEO) and low Earth orbit (LEO). The MEO satellites acquire low-resolution (LR) images for onboard target identification and subsequently schedule high-resolution (HR) observations by a set of LEO AEOSs. We adapt the AEOSSP to this multi-orbit context by explicitly considering communication constraints between MEO and LEO satellites and propose several heuristic solution methods. Among them, the quality-based greedy algorithm yields up to a 35.5% improve in observation profit in simple, low-conflict scenarios, while the structured heuristic algorithm proves the most robust, achieving up to a 21.5% increase in challenging schedules. Full article

This paper presents a systematic qualification process for an electronic beam-steering antenna assembly for a low-Earth orbit (LEO) communication satellite. The transmitting/receiving antenna for the LEO communication satellite is based on a cavity-backed slot radiator, which has improved radiation efficiency and low mutual coupling compared to conventional PCB patch structures. In order to verify the electrical performance and reliability of the manual soldering process in a tightly spaced array structure with narrow element spacing and densely connected multi-channel RF modules, a reduced model was designed and fabricated and qualification tests were conducted under launch and on-orbit environments. The integration equipment was developed to ensure precise mechanical alignment and integration/disassembly between the radiating element arrays of the transmitting and receiving antenna modules and the RF modules, thereby establishing a manufacturability strategy for the antenna module and RF integrated module, which comprise a large array structure. Finally, the qualification tests of the transmitting and receiving antenna were performed to evaluate the structural and thermal stability considering the launch and orbital environments. The systematic qualification process proposed in this paper can be used in the development of the antenna system of the communication satellite. Full article

Centralized reactive orchestration in Low Earth Orbit (LEO) networks struggles with heavy-tailed traffic surges that trigger signaling storms and topology instability. To address this challenge, we develop a LEO-specific predictive resource allocation framework that integrates spectral-aware distributional forecasting with risk-aware allocation. The forecasting module pairs cascaded dual-scale Exponential Moving Average (EMA) decomposition with a direct multi-step decoder to suppress autoregressive error accumulation. A Spectral Penalty operating in the frequency domain enhances sensitivity to orbital harmonics, while nonuniform quantization yields calibrated probabilistic bounds that preserve heavy-tailed characteristics. On the allocation side, the predictive standard deviation serves as an endogenous risk index amplified by service priority to form a capacity bound that is explicitly aware of risk. A companion demand model structurally reserves a fixed control plane bandwidth floor, insulating signaling from data plane congestion. Simulation results show that the forecasting module reduces the Continuous Ranked Probability Score (CRPS) by up to 5.9% relative to the strongest compared distributional baseline across prediction horizons of 30–105 min. Under a 300% traffic shock, the distributed allocation mechanism maintains 99.99% satisfaction for the highest priority service class and keeps control plane overflow below 0.05%. Lower-priority traffic is curtailed through compression governed by priority, and the per-node memory consumption is sufficiently low for deployment on current onboard satellite processors. Full article

The space–air–ground integrated network (SAGIN) is a key 6G architecture that provides seamless three-dimensional connectivity, exhibiting hierarchical structural symmetry between LEO satellite and HAP layers. Integrating information-centric networking (ICN) with caching on Low Earth Orbit (LEO) satellites and high-altitude platforms (HAPs) significantly enhances content distribution efficiency. Existing studies on caching mechanisms have made progress but lack optimized cache resource allocation and accurate popular content identification. Thus, an interest-aware caching scheme (ICRL) based on reinforcement learning is proposed to optimize the SAGIN’s popular content caching decisions, aiming to achieve rational symmetric allocation of cache resources across LEO and HAP layers. Different from existing RL-based caching methods, the proposed ICRL scheme considers the LEO-HAP hierarchical architecture and designs an improved reinforcement learning mechanism to adapt to the dynamic characteristics of the SAGIN. First, an air–space two-tier caching architecture is constructed to enable collaborative caching between LEO satellites and HAPs. Second, to select high-value nodes intelligently, the proposed scheme leverages a comprehensive importance model that quantitatively analyzes HAP and LEO indicators such as topology, transmission capacity, and location. Finally, a reinforcement learning-based dynamic cache mechanism is developed. It captures real-time network requests and cache states to select optimal actions and adapt to network dynamics for better content popularity matching. Extensive evaluations based on NDNSIM demonstrate that ICRL outperforms baseline schemes in terms of cache hit ratio, server load, and request latency and achieves a symmetric balance of network load and service performance in the whole SAGIN. Full article

The Cosmic Microwave Background (CMB) carries crucial information about the origin and evolution of the Universe, with its polarization patterns providing potential evidence of primordial gravitational waves. Achieving the precision required for these measurements demands highly accurate calibration methods. This study presents the development of a reference signal source to be integrated as the payload of LEO-CalSat, a Low-Earth Orbit satellite designed to serve as an artificial, far-field calibration tool for ground-based CMB polarization experiments. The system aims both to validate the technological readiness of a compact calibration payload for future L2 missions and to provide reference signals in the W-band (75–110 GHz) for current observatories. The calibration source, integrated within the volume of a CubeSat’s 2 U, was designed to balance miniaturization with performance, incorporating key components such as a frequency multiplier with a Voltage-Controlled Oscillator, horn antenna, and polarizer. Laboratory tests demonstrated fully polarized emission with output powers up to 6 dBm and a signal-to-noise ratio of approximately 30 dB, confirming the feasibility of precise polarization calibration. The reduced mass and power consumption (1 kg, 9 W) ensure compatibility with CubeSat constraints. The results validate the core concept and readiness of LEO-CalSat for space operation, representing a significant step toward establishing standardized, space-based calibration for future CMB missions and advancing precision cosmology. Full article

Satellite communication greatly extends the reach and functionality of terrestrial communication networks, providing indispensable applications in defense, security, transportation, science, and technology. However, communication satellites operating in low Earth orbits face harsh space environments that severely affect their service life and reliability. The radio frequency (RF) module constitutes the core architecture of satellites, and its reliability directly determines overall satellite performance. While existing research has predominantly focused on the failure mechanisms of power amplifiers, investigations into the failure behaviors of other RF components—such as filters, frequency converters, and connectors—remain comparatively fragmented. Moreover, a comprehensive and systematic review addressing the RF module from a holistic, cross-component perspective is notably absent in the current literature. Therefore, this study comprehensively reviews existing studies on the reliability of spaceborne electronic components, highlighting both commonalities and differences in their failure mechanisms. Particular attention is given to in-orbit failure mechanisms of critical components, including power amplifiers, frequency converters, filters, and RF connectors. From the perspective of electronic components, this study assesses the in-orbit service capability, reliability, and lifespan of communication satellites. Finally, it identifies key challenges in ensuring the reliability of satellite electronic components and proposes future research directions and technical strategies for improvement. This study provides systematic insights for researchers in satellite communication RF modules and establishes a foundation for further advancements in the field. Full article

To support intelligent maritime applications, space–air–ground–sea integrated networks (SAGSINs) have been introduced in maritime communications to provide wide coverage and reliable network services. In unmanned aerial vehicle (UAV)-assisted SAGSIN architectures, UAVs can flexibly extend coverage and provide on-demand communication and computing support. However, due to the high mobility of low Earth orbit (LEO) satellites and the limited endurance of UAVs, single-platform deployment strategies struggle to provide both flexibility and scalability in maritime communication networks. To mitigate the service instability caused by satellite orbital dynamics and limited UAV endurance, we propose a Hybrid Multi-Agent Proximal Policy Optimization (H-MAPPO)-based joint satellite–UAV deployment scheme for UAV-assisted SAGSIN systems. The proposed method optimizes joint UAV positioning and resource allocation to enhance communication coverage while reducing overall operational cost. By incorporating satellite orbital dynamics and UAV mobility into a multi-agent reinforcement learning (MARL) framework, adaptive resource scheduling can be achieved under time-varying maritime demands. Simulation results show that the proposed H-MAPPO algorithm achieves superior convergence performance, higher user coverage, and lower total system cost compared with learning-based, random, and heuristic methods while maintaining stable and robust performance under varying user densities and network scales. Full article

The Research and Observation in Medium Earth Orbit (ROMEO) mission, developed at the University of Stuttgart‘s Institute of Space Systems, seeks to demonstrate a cost-effective exploitation of the medium Earth orbit (MEO) for sustainable access to space. It uses a green propulsion system with water as propellant to reach up to 2500 km altitude starting from a 450 km sun-synchronous orbit (SSO). This paper presents the design and intended use of the ROMEO satellite as well as its two in-house developed camera systems, the public relations (PR) and the near-infrared (NIR) camera system. The PR camera system features two silicon sensors with a Bayer color pattern in a compact, lightweight package and in a cold redundant setup to reduce the impact of radiation-related degradation. Their wide field of view (128 × 96°) allows imaging of the complete visible Earth in the mission‘s final orbit and supports calibration of the Earthshine telescope, which is the primary payload. The NIR camera system uses a commercial InGaAs sensor with a high quantum efficiency up to 1700 nm, coupled to a 100 mm focal length optics assembly that yields a ground sampling distance of 45 m in the initial orbit. Its scientific objectives include monitoring gas flares and wildfires, which are relevant to climate change research, and demonstrating an exoplanet transit detection—an unprecedented capability for a small satellite using a commercial off-the-shelf InGaAs sensor in the NIR spectrum. This paper demonstrates that ROMEO’s compact, low-mass camera systems meet mission constraints while enabling a broad spectrum of scientific and outreach activities. Full article

Low-Earth orbit satellites are gradually becoming the core infrastructure of integrated aerospace communication networks, with their significant advantages of high communication rates, small transmission delay, and wide coverage. Interference with military communications in response to their security and protection needs is a current research challenge. Consequently, this paper introduces an interference technique optimized for low-Earth orbit satellite signals using a multimodal learning transformer model (OI-MLT). The proposed method incorporates symmetry-aware design by exploiting the inherent time–frequency structural characteristics of LEO satellite signals and the spatially distributed topology of interference sources. An optimized model for distributed interference sources is developed, and multimodal information of spectra and numerical values is processed in parallel through the self-attention mechanism. This approach effectively addresses the problem of dynamic matching between the interference signal and target signal in high-speed LEO scenarios, as well as high-precision interference synchronization under time-varying channels. Experimental results demonstrate that this technique enhances the precision of frequency tracking, reduces the time required for synchronization establishment, and improves the interference success rate by 27.52% on average compared with existing methods. Full article

Semantic communications have emerged as a key paradigm for intelligent sixth-generation (6G) wireless networks, which aim to convey the meaning of information rather than accurate bit sequences. However, in open-space low Earth orbit (LEO) satellite links, the broadcast nature and wide beam coverage expose semantic transmissions to severe eavesdropping risks. This paper establishes a unified theoretical and algorithmic framework for secure semantic downlink transmission in satellite networks. In particular, we first develop an integrated mathematical model that couples the semantic representation process, physical-layer satellite propagation characteristics, and information-theoretic secrecy into a single analytical formulation. By defining a joint semantic security cost function, the antagonistic trade-off between semantic fidelity and secrecy capacity is quantitatively characterized under realistic power, beamforming, and propagation constraints. To balance semantic fidelity and information secrecy, a reinforcement-learning-based optimization framework is proposed, wherein an actor–critic agent learns optimal power allocation and semantic weighting strategies through continuous interaction with the environment. This learning-based optimization approach enables autonomous control without requiring explicit channel distribution knowledge or offline parameter tuning. Extended simulation results show that the proposed approach consistently enhances both semantic fidelity and secrecy performance compared with conventional power-control schemes and demonstrate its potential as a foundational architecture for secure and intelligent semantic communications in next-generation satellite networks. Full article