Core-based Shared Tree Multicast Routing Algorithms for LEO Satellite IP Networks

The improvement of BDS Observation Geometry with LEO constellations in Orbit Determination

International organizations such as the 3rd Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU) regard non-terrestrial networks (NTNs) as an essential component of the sixth-generation (6G) mobile communication technology and have advanced relevant standardization efforts. Low Earth orbit (LEO) satellite communication (SatCom) constitutes a key part of NTNs, and efficient uplink random access (RA) is crucial for establishing initial connections in LEO SatCom systems. However, the long propagation delay and wide coverage of LEO satellites substantially increase access latency and collision probability due to the limited number of beams and their constrained coverage areas. In addition, the highly non-uniform spatial distribution of user equipment (UE) further aggravates access inefficiency. To this end, this paper investigates joint beam position grouping and RA channel (RACH) occasions (ROs) allocation (JBPGRA) for LEO SatCom systems. Specifically, we develop a system model for RA under beam hopping and identify the key factors that influence RA performance. Furthermore, we derive expressions for both the instantaneous signal-to-interference-plus-noise ratio (SINR) and the average SINR under a given non-uniform UE spatial distribution. Building on this analysis, the JBPGRA problem is formulated as an integer linear programming problem that seeks to maximize RA success while conserving RO resources under non-uniform UE distribution. To achieve a practical solution, we propose an efficient JBPGRA algorithm composed of beam position classification, sparse beam position grouping, and RO allocation modules. Simulation results demonstrate that, under the same UE density, the proposed JBPGRA scheme achieves over 29% higher access success rate in dense beam positions compared with the uniform baseline adopted in existing SatCom systems, while reducing RO consumption by more than 49% and decreasing the number of beam position groups by over 57%.

The increasing demand for ultra-low-latency and high-throughput communication necessitates network architectures capable of ensuring reliable real-time connectivity. To address this requirement, integrated architectures combining terrestrial networks (TN) and non-terrestrial networks (NTN)—particularly hierarchical multi-layer satellite systems composed of geostationary Earth orbit (GEO), medium Earth orbit (MEO), and low Earth orbit (LEO) satellites—have attracted considerable attention. Such hierarchical systems offer global coverage, low latency, and high capacity. However, their highly dynamic topology, fast-moving satellites, and fluctuating link quality pose significant challenges to establishing efficient end-to-end routing paths. Traditional routing methods based on static paths or centralized control lack the adaptability required for such environments. To address these challenges, this paper introduces a neural hierarchical reinforcement learning (HRL)-based routing algorithm for satellite networks. The framework exploits the layered structure of GEO, MEO, and LEO satellites, enabling gateway-assisted routing and adaptive path selection. Control responsibilities are distributed across orbital layers: the GEO layer allocates hop-count budgets, the MEO layer selects feasible LEO paths, and the LEO layer forwards data packets. When inter-satellite links are unavailable, terrestrial gateways provide alternative routes. Routing decisions further account for satellite-to-satellite and satellite-to-ground link quality, with an end-to-end delay-based reward function capturing transmission, processing, and propagation effects. Simulation results show that the proposed neural hierarchical framework, supported by integrated terrestrial and non-terrestrial networking, achieves faster convergence, greater route stability, and better adaptability than conventional routing algorithms and HRL approaches.

Gravity field recovery of inter-satellite links between Beidou navigation satellite system (BDS) and LEO based on geodesy and time reference in space (GETRIS)

Low earth orbit (LEO) satellite system becomes an emerging non-terrestrial network solution for future radio communications. To maximize the system capacity, an LEO satellite could utilize circularly polarized planar antenna array with the frequency reuse technique to form multiple earthmoving beams. With the increasing number of LEO satellites, inline interference from the geosynchronous equatorial orbit (GEO) satellite is an important issue to be resolved when the spectrum resource is shared. With the frequency hopping phenomena in earth-moving LEO networks, the inline interference could be minimized if the GEO users also perform the similar frequency hopping via coordination between LEO and GEO satellite networks. This paper proposes eight spectrum sharing schemes for GEO satellite networks to produce the frequency hopping sequence in earth-moving LEO network. We evaluate the system capacity, fairness between LEO and GEO users, and time costs of all schemes. Simulation results validate the advantage obtained from coordination, and show that the simple greedy scheme performs as good as the optimal scheme due to fixed frequency hopping patterns.

Low earth orbit (LEO) satellite has the advantages of global coverage, low propagation delay and low path losses, etc.Combined with orthogonal frequency division multiplexing (OFDM) transmission, LEO satellite system can support very high capacity suitable for wideband multimedia traffic.This paper analyzes the channel model and throughput for LEO OFDM satellite communication system.First, several channel models for LEO satellite system are expounded with simulations because channel model is the basis of throughput analysis.Second, the beam model and the signaling model for LEO satellite are introduced.Considering high speed movement of LEO satellite, channel fading and the channel quality information (CQI), link throughput is simulated.Finally, compared with MF-TDMA system, simulation results demonstrate that OFDM system outperforms the former because its feasibility and superiority in throughput.

Machine learning (ML) frameworks are investigated for use in simultaneous tracking and navigation (STAN) with low Earth orbit (LEO) satellites. STAN is a navigation paradigm that utilizes specialized LEO receivers to extract navigation observables (e.g., pseudorange and Doppler) from LEO satellite signals. Two neural network architectures are compared: Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN). Additionally, two ML-based orbit determination frameworks are compared: ephemeris propagation and residual error propagation. The objective of the comparison is to select an approach with the lowest open-loop propagation error as well as computational cost. Based on simulation results, a nonlinear autoregressive neural network with exogenous inputs (NARX) embedded within the residual error modeling framework is selected as the best ML approach among the compared candidates. Experimental results are presented demonstrating a ground vehicle navigating for a total of 258 seconds, while receiving signals from two Orbcomm LEO satellites. Global navigation satellite system (GNSS) signals were artificially cut off for the last 30 seconds, during which the vehicle traversed a trajectory of 871 m. Two navigation frameworks are compared to estimate the vehicle’s trajectory: (i) LEO signal-aided inertial navigation system (INS) STAN framework using Simplified General Perturbation (SGP4) as its propagator and (ii) the proposed LEO signal-aided INS STAN framework using ML as its propagator. The STAN with SGP4 achieved a three-dimensional (3-D) position root-mean squared error (RMSE) of 30 m. In contrast, the proposed STAN with SGP4+NARX framework achieved a 3-D position RMSE of 3.6 m.

The efficient utilization of limited spectral resources in space presents a significant challenge given the rapidly increasing number of satellites (SATs) in various orbits. To address this challenge, it is paramount to enhance spectral efficiency by facilitating inter-operation between SATs in different orbits. In this letter, we investigate the impact of dynamic spectrum access (DSA) on the spectral efficiency of a SAT network that integrates geostationary (GEO) and low Earth orbit (LEO) SATs. Specifically, in the context of DSA, the secondary LEO SATs can opportunistically reuse the frequency bands originally assigned to primary GEO SATs. To characterize the resultant spectral efficiency in a realistic time-varying SAT network topology, we analyze the signal-to-interference-plus-noise ratio (SINR) at the nadir user of a randomly selected LEO SAT and an upper bound on average throughput of the LEO SAT. Simulation results demonstrate the significant advantages of DSA in enhancing the spectral efficiency of the integrated GEO-LEO SAT network.

An opportunistic navigation framework using low Earth orbit (LEO) satellites is analyzed. This framework, termed simultaneous tracking and navigation (STAN), estimates a navigating vehicle’s state along with the states of orbiting LEO satellites. STAN employs an extended Kalman filter (EKF) to fuse measurements from satellite receivers and an inertial navigation system (INS). The navigation performance is analyzed due to: (i) the vehicle being equipped with (1) different inertial measurement unit (IMU) grades: consumer, industrial, and tactical and (2) different receiver clock quality: temperature-compensated crystal oscillators (TCXO) and oven-controlled crystal oscillators (OCXO) and (ii) the LEO satellites being equipped with different transmitter clock quality: OCXO and chip-scale atomic clock (CSAC). Additionally, the effect of utilizing a large number of LEO satellites for navigation is investigated. This analysis provides insight into the achievable performance of STAN, which can serve as an alternative navigation system in global navigation satellite system (GNSS)-denied environments. The performance predictions from simulations are compared with experimental results with real signals from the Orbcomm LEO constellation. A close match between the simulation and experimental results is demonstrated for an unmanned aerial vehicle (UAV) navigating via the STAN framework with signals from two Orbcomm LEO satellites for 160 seconds, the last 35 seconds of which are without GNSS signals. The UAV’s position root-mean squared error (RMSE) from simulations was 8.6 m, while the experimental position RMSE was 10 m.

Any robust communication system can be defined as one that can provide ubiquitous connectivity with lower latency periods. Since 1990, Low Earth Orbit (LEO) satellite constellations have proven to be plausible and efficient alternatives to high altitude GEO satellites in delivering seamless voice and data communication services. This is due to some of their important advantages over geo- stationary such as low power requirement, efficient utilization of frequency spectrum and, low propagation delays pertaining to the satellite’s closer proximity to the ground station. On the downside, the difference in the mobility speed of a LEO satellite and a user on earth is so high that the user can be considered constant, unless he/she is on a fast-moving vehicle. This calls for strategical user handover mechanisms between LEOs to avoid call drops, as usually the service time for a user tend to be higher than the coverage time of a typical non-stationary LEO. Consequently, it is admissible to claim that the quality of service provided by such a network is limited by the performance of LEO handoff algorithms (generally based on parameters like Probability of call blocking/call termination, Throughput, Delay in transmission). In this paper, we will discuss the LEO handoff algorithms developed over the years and particularly delve into the most commendable satellite handover techniques exploiting the property of satellite diversity, that fall with Acceptable Forced termination of existing calls and dropping of new calls.

Low Earth Orbit (LEO) satellite communication networks have become an important means to provide internet access services for areas with limited infrastructure. Compared with the Geostationary Earth Orbit (GEO) satellites, the LEO satellites have limited on-board communication caching and calculating resources. Furthermore, the distribution of traffic requests is dynamically changing and uneven due to the relative movement between the LEO satellites and the ground. Therefore, how to schedule the multi-dimensional resources is an important issue for the LEO satellite communication networks. Beam-hopping is an efficient approach to improve the resource utilization by dynamically allocating time, power, and frequency according to the traffic requests. This paper proposes an efficient multi-dimensional resource allocation mechanism for beam-hopping in LEO satellite networks, which simultaneously satisfies the GEO interference avoidance. First, we construct the beam-hopping model of LEO satellites, and formulate the resource optimization problem. Second, we provide the weighted greedy strategy to determine the illumination pattern. In order to reduce the search space, the cells are clustered to non-interference clusters. Then, an improved genetic algorithm is provided to jointly allocate the communication resources. Finally, we construct various simulations to evaluate our proposed mechanism. Compared with the random-BH, polling-BH and traditional genetic algorithm, our algorithm achieves better performance in terms of both system throughput, access success rate, average delay and fairness between cells. The performance improvement is more significant in scenarios where traffic demand is unevenly distributed.

6G non-terrestrial networks (NTNs) will use satellites for global internet access, where low earth orbit (LEO) satellites will be used frequently due to the lower latency and higher link budget that can be provided compared to higher altitude satellites. However, LEO satellites require many satellites due to their fast movement and limited coverage, leading to frequent handovers (HOs). This paper presents an intelligent LEO satellite conditional handover (ILCHO) scheme that improves the NTN performance by addressing the frequent HO issues while reducing the signal strength variation between cells and outdated measurement values. The ILCHO scheme derives the LEO satellite's state information (position and orbital path), which is used to obtain the conditions for a LEO satellite to be accessible to enable optimized target LEO satellite selection that will maximize the overall network service performance while minimizing the number of HOs. The ILCHO scheme uses multi-agent reinforcement learning (MARL) to maintain an optimized performance through early preparation by incorporating a distance-based event in the execution phase. The simulation results show that the proposed ILCHO scheme can increase the throughput and signal to noise ratio (SNR) performance compared to other HO algorithms, thereby providng improved and more stable communication conditions.

<p><strong>Abstract</strong> Global Navigation Satellite Systems (GNSS), which are fundamental to Positioning, Navigation, and Timing (PNT), play also a critical role for the determination of ITRF and the monitoring of global geodetic parameters, such as geocenter and Earth Rotation Parameters (ERP), thanks to the globally distributed network. The Low Earth Orbit (LEO) satellites with onboard GNSS receivers as moving stations can significantly improve the observation geometry of a ground tracking network, and thus enhance the solutions. Previous studies have demonstrated that integrated processing of ground GNSS stations and LEO satellites can improve the GNSS satellite orbits and the determination of the geocenter, especially for spare networks. In this study, we further investigate the contribution of having five LEO satellites (two GRACE-FO satellites and three SWARM satellites) to a rather robust ground GNSS network, i.e., 120 stations. We focus not only on the GNSS satellite orbits, and the geocenter and ERP, but also on some other parameters like ambiguities. We further investigate the impact of the LEO orbit modelling and the weighting strategies of the LEO observations in order to properly consider the fact that onboard GNSS observations can be modelled more precisely than that from ground stations due to the differences in station environment and atmosphere.</p>

Recently, a large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space. Due to multimodal sensors equipped by the LEO satellites, they serve not only for communications but also for various machine learning applications. However, a ground station (GS) may be incapable of downloading such a large volume of raw sensing data for centralized model training due to the limited contact time with LEO satellites (e.g. 5 minutes). Therefore, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">federated learning</i> (FL) has emerged as the promising solution to address this problem via on-device training. Unfortunately, enabling FL on LEO satellites still face three critical challenges: i) heterogeneous computing and memory capabilities, ii) limited downlink/uplink rate, and iii) model staleness. To this end, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedSN</b> as a general FL framework to tackle the above challenges. Specifically, we first present a novel sub-structure scheme to enable heterogeneous local model training considering different computing, memory, and communication constraints on LEO satellites. Additionally, we propose a pseudo-synchronous model aggregation strategy to dynamically schedule model aggregation for compensating model staleness. Extensive experiments with real-world satellite data demonstrate that FedSN framework achieves higher accuracy, lower computing, and communication overhead than the state-of-the-art benchmarks.

The atmosphere reflects the distinct signatures associated with long-term changes due to solar cycle, short-term changes related to solar phenomena such as solar flares, coronal mass ejections (CMEs), high solar wind streams (HSS) and corotating interaction region (CIRs), as well as changes related to latitude variation. The orbital trajectory of low Earth orbiting satellites (LEOSs) are affected accordingly, as direct consequence of the variability of atmospheric temperature (T) and density (ρ). Atmospheric perturbation increases drag on LEOSs leading to accelerated orbital decay. In this chapter we present a compact review of atmospheric responses to solar activity and how they impact the orbital trajectory of low Earth orbiting (LEO) satellites. This review includes the recent progress we made in the studies related to space weather effects on the ionosphere and LEO satellites trajectory.