Collaborative interference avoidance technology in GEO‐LEO co‐existing satellite system

In recent years, low earth orbit (LEO) satellite constellation systems have been developed rapidly. However, the scarcity of satellite spectrum resources has become one of the major obstacles to this trend. LEO satellite constellation communication systems sharing the spectrum of incumbent geostationary earth orbit (GEO) satellite system is a feasible way to alleviate spectrum scarcity. Therefore, it has practical significance to study the optimization of satellite resources allocation (RA) in a spectrum sharing scenario. This paper focuses on the RA problem that LEO satellites share a GEO high throughput satellite’s spectrum in a beam-hopping (BH) manner. The GEO satellite system is served as the primary system and the LEO satellite constellation system is served as the secondary system whose frequency bands and transmitting power are strictly limited. Compared with conventional multibeam satellites, BH satellites have the advantage of flexibility in the time dimension. Therefore, we make full use of the flexibility of LEO BH satellites to realize the matching of traffic demand and traffic supply. The RA problem is decomposed into three sub-problems, namely, frequency band selection (FBS) problem, illuminated cell selection (ICS) problem, and transmitting power allocation (TPA) problem. We solve each sub-problem in order and finally form a complete RA scheme. The performance evaluation of the proposed RA scheme is carried out in real-time and simulation results show that the LEO BH satellite paired with the RA scheme we proposed has good adaptability to the uneven distribution of traffic demand in the spectrum sharing scenario.

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.

This paper proposes a double-layered satellite network based on the rateless code, which can globally distribute the information with a few satellites. The satellite network is composed of Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) satellites. The GEO satellites which cover the low and middle latitudes serve as the backbone network, whereas the LEO satellites are considered the enhanced network to make up for the shortage of the backbone network. Due to the interruption tolerance of the rateless code, only a few satellites are employed to achieve worldwide information distribution in the design of the satellite network. The coverage performance and the average elevation angle of the user are simulated, and results demonstrate that the network can achieve globally information distribution.

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

In this paper, a collision avoidance maneuver was sought for low Earth orbit (LEO) and geostationary Earth orbit (GEO) satellites maintained in a keeping area. A genetic algorithm was used to obtain both the maneuver start time and the delta-V to reduce the probability of collision with uncontrolled space objects or debris. Numerical simulations demonstrated the feasibility of the proposed algorithm for both LEO satellites and GEO satellites.

The spectrum sharing between the low earth orbit (LEO) and geostationary earth orbit (GEO) satellite communication systems provides a feasible solution to relieve the spectrum shortage and support the rapid expansion of LEO satellite systems. In order to improve the spectrum efficiency and avoid serious inter-system interference (ISI), we investigate the resource allocation for beam-hopping (BH) based LEO satellite systems which share the spectrum resources with a BH-based GEO satellite. The BH technology enhances the resource scheduling flexibility of the satellite systems, but it also makes the ISI more complicated and intractable. Considering the limited computing capability of the LEO satellite, the resource allocation problem is decomposed into the time slot allocation (TA) sub-problem and the transmit power optimization (TPO) sub-problem. A greedy algorithm is proposed for the TA sub-problem, and the TPO sub-problem is solved by using the convex optimization theory. Simulation results show that the proposed resource allocation scheme not only achieves higher system throughput, but also suppresses the ISI at a low level, which ensures the harmonious coexistence of the two satellite systems.

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.

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.

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.

Recently, the development of Low Earth Orbit (LEO) satellites and the advancement of the Mobile Edge Computing (MEC) paradigm have driven the emergence of the Satellite Mobile Edge Computing (Sat-MEC). Sat-MEC has been developed to support communication and task computation for Internet of Things (IoT) Mobile Devices (IMDs) in the absence of terrestrial networks. However, due to the heterogeneity of tasks and Sat-MEC servers, it is still a great challenge to efficiently schedule tasks in Sat-MEC servers. Here, we propose a scheduling algorithm based on the Deep Reinforcement Learning (DRL) method in the Sat-MEC architecture to minimize the average task processing time. We consider multiple factors, including the cooperation between LEO satellites, the concurrency and heterogeneity of tasks, the dynamics of LEO satellites, the heterogeneity of the computational capacity of Sat-MEC servers, and the heterogeneity of the initial queue for task computation. Further, we use the self-attention mechanism to act as a Q-network to extract high-dimensional dynamic information of tasks and Sat-MEC servers. In this work, we model the Sat-MEC environment simulation at the application level and propose a DRL-based task scheduling algorithm. The simulation results confirm the effectiveness of our proposed scheduling algorithm, which reduces the average task processing time by 22.1%, 30.6%, and 41.3%, compared to the genetic algorithm(GA), the greedy algorithm, and the random algorithm, respectively.

We study the lightpath provisioning problem of data relay services (DRSs) in a geostationary Earth orbit (GEO) satellite network. In DRS, a low Earth orbit (LEO) satellite user (e.g., remote sensing satellite) delivers the image data to a dedicated ground station (GS) through a GEO network. The LEO satellite needs to circle the Earth several rounds in order to fulfill the transmission, which results in it being connected to different GEO satellites at different times. This phenomenon reveals a fundamental difference between DRS and the services in a terrestrial network, where the source and destination nodes are typically fixed. The lightpath provisioning problem of DRSs is challenging, as we should jointly address routing, bandwidth assignment, and the time schedule of the lightpaths for each DRS. In this paper, we first devise the problem mathematically as a mixed integer linear program (MILP) and then propose a heuristic algorithm called data relay request provisioning as the MILP does not scale well. In the numerical analysis, we demonstrate the effectiveness of the heuristic algorithm and provide a sensitivity analysis on several parameters of our problem.

For real-time precise orbit determination (POD) of low earth orbit (LEO) satellites, high-accuracy global navigation satellite system (GNSS) orbit and clock products are necessary in real time. Recently, the Japanese multi-GNSS advanced demonstration of orbit and clock analysis precise point positioning (PPP) service and the new generation of the Australian/New Zealand satellite-based augmentation system (SBAS)-aided PPP service provide free and precise GNSS products that are directly broadcast through the navigation and geostationary earth orbit satellites, respectively. With the high quality of both products shown in this study, a 3D accuracy of centimeters can be achieved in the post-processing mode for the reduced-dynamic orbits of small LEO satellites having a duty cycle down to 40% and at sub-dm to dm level for the kinematic orbits. The results show a promising future for high-accuracy real-time POD onboard LEO satellites benefiting from the precise free-of-charge PPP corrections broadcast by navigation systems or SBAS.

Today most operational Earth observing satellites reside in low Earth orbits (LEO) at less than 1,000 km altitude, and in geostationary Earth orbits (GEO) at ~35,800 km altitude. These orbits have been the venues of choice for observations, albeit for very different reasons. LEO provides high spatial resolution with low temporal resolution while GEO provides for low spatial resolution, but high temporal resolution. NOAA utilizes both venues for their environmental satellites. The NOAA Polar-orbiting Operational Environmental Satellites (POES) reside in LEO Sun synchronous orbits at approximately 830 km in altitude, as do the Defense Meteorological Satellite Program (DMSP) satellites of the Department of Defense. In the near future the POES and DMSP satellites will be merged into a new satellite system referred to as the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The NOAA Geostationary Operational Environmental Satellite (GOES) system, as the name specifies, resides at the other preferred observational venue of GEO. The Jet Propulsion Laboratory (JPL), under contract to NOAA, has been studying the characteristics of medium Earth orbits (MEO), at altitudes between 1000 and 35,800 km, as an observation venue to answer the question as to whether MEO might capture the attributes of the two traditional venues. This on-going study initially focused on determining the optimal altitude for MEO observations, through numerous trade studies involving altitude, instrument complexity, coverage, radiation environment, data temporality, revisit time, data rates, downlink requirements and other parameters including cost and launch complexity. Once the optimal altitude of 10,400 km had been determined the study proceeded to explore single through multiple MEO satellite constellation performance capabilities using two instrument types, a visible through infrared (IR) imager and IR sounder as the satellites’ payload. The MEO performance capabilities were compared to comparable LEO and GEO satellite constellation capabilities. This portion of the study concluded that indeed for global coverage a constellation of satellites operating in the MEO venue could capture the attributes of those operating in the LEO and GEO venues. Three 8-satellite constellations configurations - Walker, ICO, and Equatorial-Polar (EP) - then were studied to develop more constellation coverage statistics including robustness to individual satellite failure. That study phase concluded that the EP constellation was superior to both the ICO and Walker configurations. The study is presently examining if, and to what extent, the equatorial portion of the EP constellation might provide substantive supplemental data to that collected by the NPOESS and GOES satellite constellations.

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

With the advancement of low earth orbit (LEO) satellite communications, direct-to-satellite technology has attracted widespread attention. Due to the constrained onboard resources of LEO satellite, an efficient resource allocation strategy is essential to meet diverse user traffic demands. Furthermore, the spectrum sharing among multi-layer LEO satellites requires effective interference avoidance mechanism to enhance the overall service quality of the satellite system. To achieve efficient resource management in a dual-layer LEO satellite system, we propose a resource allocation strategy based on multi-agent proximal policy optimization (MAPPO). This strategy consists of three steps. Firstly, beam hopping time plan (BHTP) is designed based on a low complexity strategy. Next, the users served in each time slot are selected according to the buffer status. Finally, the MAPPO framework is employed to enable the flexible allocation of bandwidth and power resources. Simulation results demonstrate that the proposed strategy not only satisfies the diverse traffic demands of terminal users but also effectively reduces satellite power consumption.