An analysis of the impact of Earth rotation on LEO satellite mobility models

Low earth orbit (LEO) satellite network has the advantages of comprehensive coverage and has the unique benefits of short satellite-to-ground transmission distance and low construction cost, which can effectively complement the limited coverage of ground mobile communication network. Hence, LEO satellites gain extensive attention in the field of mobile communication. However, restricted by the existing mode (BP, Bent Pipe) of high transmission latency problems in the LEO space satellite computing (LSSC), it is challenging to meet the low latency requirement of time-sensitive tasks. Therefore, on-orbit collaborative computing technology for LSSC is proposed in this paper. While due to the high dynamic and non-centrality of the LEO satellite system, collaborative computing in satellite networks will face many difficulties. Aiming at the high dynamic and non-centrality of the LEO satellite network, this paper proposes a dispersed computing paradigm for the LEO satellite network, which is suitable for high dynamic no-center scenarios. In this dispersed computing paradigm, a steady-state matrix based on the time-expanded graph (TEG) model is introduced in this paper to steady-state the topology of a high dynamic LEO satellite network. According to the matrix, a transmission capacity and computing capacity based diffusion algorithm (TCGDA) is proposed to perform optimal task allocation in the LEO satellite network. The simulation results show that the proposed dispersed computing method can effectively complete the computing task with the optimized latency.

Low Earth orbit (LEO) satellite networks have emerged as a promising solution, offering advantages such as lower propagation delay, broader coverage, and rapid deployment capabilities. However, the dynamic topology and frequent handovers inherent in LEO satellite systems, coupled with limited onboard computational resources, necessitate the development of efficient and lightweight routing algorithms. Therefore, this paper proposes quantum reinforcement learning-based satellite routing (QRL-SR) tailored for LEO satellite networks. The QRL-SR algorithm addresses three critical considerations: (i) adapting to the dynamic and time-varying environment of LEO satellite networks; (ii) incorporating LEO satellite geometry by transforming celestial coordinate data, specifically two-line element, into orbital coordinate systems for accurate LEO satellite positioning over time; and (iii) being designed to be lightweight by leveraging QRL to reduce the number of training parameters. The proposed QRL-SR efficiently trains routing policies with fewer parameters, aligning with LEO satellites’ small-size, weight, and power (SWaP) constraints. The primary purpose of the QRL-SR-based LEO satellites is to reduce free space path loss, delay time, and the number of hops needed for routing through the inter-satellite links. Finally, experimental results demonstrate that the QRL-SR achieves routing performance comparable to or outperforms conventional algorithms while significantly reducing computational resources.

Low Earth Orbit (LEO) satellite networks need to consider the high mobility of LEO satellites. The high Doppler shift due to the mobility of LEO satellites occurs essentially and its scale is not considered in the terrestrial network. Therefore, it is difficult to overcome the Doppler shift in LEO satellite networks with the existing terrestrial network design. In this paper, we propose a LEO sate0llite beam design considering Doppler shift. After calculating Doppler shift and compensation, we analyze the characteristics of Doppler shift in LEO satellite networks. Given OFDM numerology technology of 5G NR (New Radio) and carrier frequency, the LEO satellite beam size is determined differently.

Low earth orbit (LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the "ubiquitous connection" of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading (DDLCCO) algorithm, where multiple parallel deep neural networks (DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.

We leverage covert communication to enhance the security of a large-scale multi-tier Low Earth Orbit (LEO) satellite network against vigilant adversarial terrestrial Base Stations (BSs) aiming at detecting satellite transmissions. This approach involves deploying massive LEO satellites at different altitudes around Earth to form a multi-tier network serving as a backhaul for near-ground Unmanned Aerial Vehicles (UAVs) that provide network services to terrestrial mobile users. Meanwhile, terrestrial BSs attempt to detect satellite transmissions based on their own received signal powers. To evade detection, the LEO satellite network performs power control to obscure the satellite transmission within the co-channel interference among the LEO satellites. We formulate a two-stage Stackelberg game to model the conflict dynamics between the terrestrial BSs and the LEO satellite network. In this game, the terrestrial BSs act as non-cooperative followers at the lower stage aiming to minimize their detection errors. On the other hand, the LEO satellite network acts as the leader at the upper stage aiming to maximize its utility while ensuring communication covertness. In contrast to existing works that focus on a small set of network nodes, our study considers a large-scale multi-tier LEO satellite network and employs stochastic geometry to model the spatial distribution of network nodes. To achieve the Stackelberg equilibrium, we develop a bi-level algorithm based on Successive Convex Approximation (SCA) and golden-section search. Our numerical results provide practical insights, revealing a trade-off in leveraging co-channel interference (i.e., while it improves the communication covertness of satellite transmission, it simultaneously degrades the link reliability).

Low Earth Orbit (LEO) satellite networks are transforming global connectivity by enabling high-speed, low-latency Internet access. Particularly, they significantly facilitate areas where terrestrial networks are not deployed or destroyed. Meanwhile, LEO satellite technology is experiencing an unprecedented surge in development. This paper provides a comprehensive and up-to-date overview of LEO satellite networks. First, the evolution of LEO satellites is introduced, followed by an exploration of the components and communication architecture within LEO satellite networks using representative examples. Second, key technologies, including routing, handover management, and digital twins, are summarized, and some practical application scenarios are discussed. The performance of LEO satellite networks, illustrated by SpaceX’s Starlink, is then evaluated to understand its scheduling algorithm and network characteristics, which can inform future satellite-related algorithms and architecture design. Finally, as LEO constellations continue to expand, practical operations face significant challenges in management, technology, and security. Consequently, we highlight some open research issues to provide potential inspiration for academia and industry in satellite networking.

Mobility management is one of the key technologies in low earth orbit(LEO) satellite network. In this paper, an OPNET based simulation platform of mobility management for LEO satellite network is proposed to take advantage of OPNET in the simulation of satellite network. The performance simulation of mobility management on the platform is actualized. The location update rates of different schemes and different handoff rates are gained. This simulation platform provides a foundation for improving the performance of mobility management for LEO satellite constellation system.

In Low Earth Orbit (LEO) satellite networks, it is a challenge to allocate the limited resources to meet the needs of different calls. In this paper, a dynamic channel reservation strategy based on priorities of multi-traffic and multi-user in LEO satellite networks is proposed. The dynamic admission threshold reserved for different calls is the key of this strategy. Firstly, the traffic prediction model based on LEO satellite mobility is established. Then the channel allocation model is built on the Markov process. Finally, the reserved admission thresholds are dynamically changed according to the predicted traffic. And the calculation of the admission thresholds is solved by the genetic algorithm. The simulation results show that the proposed strategy not only meets the needs of calls of different type traffic and different level users, but also improves the overall quality of service in LEO satellite networks.

Low earth orbit (LEO) satellite networks can break through geographical restrictions and achieve global wireless coverage, which is an indispensable choice for future mobile communication systems. In this article, we present a hybrid cloud and edge computing LEO satellite (CECLS) network with a three-tier computation architecture, which can provide ground users with heterogeneous computation resources and enable ground users to obtain computation services around the world. With the CECLS architecture, we investigate the computation offloading decisions to minimize the sum energy consumption of ground users, while satisfying the constraints in terms of the coverage time and the computation capability of each LEO satellite. The considered problem leads to a discrete and nonconvex since the objective function and constraints contain binary variables, which makes it difficult to solve. To address this challenging problem, we convert the original nonconvex problem into a linear programming problem by using the binary variables relaxation method. Then, we propose a distributed algorithm by leveraging the alternating direction method of multipliers (ADMMs) to approximate the optimal solution with low computational complexity. Simulation results show that the proposed algorithm can effectively reduce the total energy consumption of ground users.

In this paper, we develop and assess online decision-making algorithms for call admission and routing for low Earth orbit (LEO) satellite networks. It has been shown in a recent paper that, in a LEO satellite system, a semi-Markov decision process formulation of the call admission and routing problem can achieve better performance in terms of an average revenue function than existing routing methods. However, the conventional dynamic programming (DP) numerical solution becomes prohibited as the problem size increases. In this paper, two solution methods based on reinforcement learning (RL) are proposed in order to circumvent the computational burden of DP. The first method is based on an actor-critic method with temporal-difference (TD) learning. The second method is based on a critic-only method, called optimistic TD learning. The algorithms enhance performance in terms of requirements in storage, computational complexity and computational time, and in terms of an overall long-term average revenue function that penalizes blocked calls. Numerical studies are carried out, and the results obtained show that the RL framework can achieve up to 56% higher average revenue over existing routing methods used in LEO satellite networks with reasonable storage and computational requirements.

Low earth orbit (LEO) satellite networks like Iridium have played a pivotal role in providing ubiquitous network access services to areas without terrestrial infrastructure because of their potential for global coverage and high bandwidth availability. With low orbit and short range as compared to geostationary satellites, LEO satellites are accessible by mobile devices with limited transmission power and small gain antennas. The drawback, however, is that LEO satellites move fast across the sky with average contact time in the order of 10 minutes, thus requiring frequent handover from one satellite to the next. To achieve smooth handover and efficiently utilize constellation capacity, we propose to use Multipath TCP (MPTCP) in LEO systems and maintain parallel, simultaneous connections between terrestrial handpoints via multiple satellites. In this paper, we discuss the feasibility of using MPTCP over LEO satellite networks and propose a framework of MPTCP-Routing design. Then the performance of this protocol is evaluated through simulation. We show that compared to traditional “single-path” TCP, MPTCP significantly improves throughput performance and prevents the interruption of transmission during handover. Furthermore, we show that our MPTCP-Routing interaction is essential for the end-to-end session to quickly recover from handover.

Since low earth orbit (LEO) satellite constellations have important advantages over geosynchronous earth orbit (GEO) systems such as low propagation delay, low power requirements, and more efficient spectrum allocation due to frequency reuse between satellites and spotbeams, they are considered to be used to complement the existing terrestrial fixed and wireless networks in the evolving global mobile network. However, one of the major problems with LEO satellites is their higher speed relative to the terrestrial mobile terminals, which move at lower speeds but at more random directions. Therefore, handover management in LEO satellite networks becomes a very challenging task for supporting global mobile communication. Efficient and accurate methods are needed for LEO satellite handovers between the moving footprints. In this paper, we propose a new seamless handover management scheme for LEO satellites (SeaHO-LEO), which utilizes the handover management schemes aiming at decreasing latency, data loss, and handover blocking probability. We also present another interesting handover management model called satellite mobility pattern based handover management in LEO satellites (PatHO-LEO) which takes mobility pattern of both satellites and mobile terminals into account to minimize the handover messaging traffic. This is achieved by the newly introduced billboard manager which is used for location updates of mobile users and satellites. The billboard manager makes the proposed handover model much more flexible and easier than the current solutions, since it is a central server and supports the management of the whole system. To show the performance of the proposed algorithms, we run an extensive set of simulations both for the proposed algorithms and well known handover management methods as a baseline model. The simulation results show that the proposed algorithms are very promising for seamless handover in LEO satellites.

LEO (Low Earth Orbit) satellite networks are capable of providing broadband access to end users in any part of the world. With the characteristics of high-speed movement and rapid dynamic topology of LEO satellites, networking has always been one of the key issues in LEO satellite network. MPLSTP (Multi-Protocol Label Switching-Transport Profile) is an excellent packet transport network (PTN) technology in terrestrial networks, which can support multi-service and have perfect function of QoS (Quality of Service), OAM (Operation & Administration & Maintenance) and survivability. In this paper, the characteristics of LEO satellite network and functional architecture of MPLS-TP are introduced. Then an advanced LEO satellite network model based on MPLS-TP (ALSN-M) is proposed, which can guarantee a connection-oriented seamless data transmission. Furthermore topology and routing algorithms of proposed LEO satellite network model is analyzed. In the end, the paper describes how to setup and remove a LSP (Label Switched Path) and how to maintain an exiting LSP when a handover happens.

In this paper, a geometrical analysis is proposed to analyse the performance of call blocking probability in low earth orbit (LEO) satellite networks. Call blocking probability is an important measure to analyse the performance of LEO satellite networks. Assuming shape of the corresponding cells in the footprint of a typical LEO satellite network is hexagonal, a theorem on calculating the maximum call blocking probability is proven. Furthermore, by applying the theorem, the impact of the peak traffic, half depression angle and the height of LEO satellite networks on call blocking probability is also analysed. The results we get can be used as a reference for the performance optimization of LEO satellite networks.

In low earth orbit (LEO) satellite networks, because the speed of LEO satellite is much faster than that of mobile node, a satellite unable to provide services for a user continuously. In order to ensure the user's communication will not be interrupted, the satellites service the users one by one. In this paper, based on weighted bipartite graph in LEO satellite networks, we proposed an access and handover strategy for the links between satellites and users. The quality of service that the satellite can provide for users is regarded as the weight of the edge in the bipartite graph, which is regarded as a multi-objective optimization problem. Since the multi-objective problem can't make all the objectives to be optimal at the same time, this paper uses the entropy method to weight each target and transforms it into a single objective optimization problem. The simulation results show that the proposed method is better than the existing methods for users and the system.