Timeslot-Adaptive and Traffic Load-Aware Routing Computation in Two-Layer LEO Satellite Networks

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.

Low Earth Orbit (LEO) Satellite Networks (SN) offers communication services with low delay, low overhead, and flexible networking. As service types and traffic demands increase, the multi-service routing algorithms play an important role in ensuring users’ Quality of Service (QoS) requirements in LEO-SN. However, the multi-service routing algorithm only considers the link QoS information, ignoring the uneven distribution of ground users, causing satellite link or node congestion, increasing the packet transmission delay, and packet loss rate. In order to solve the above problems, we propose a Multi-Service Routing with Guaranteed Load Balancing (MSR-GLB) algorithm which balances the network load while satisfying multi-service QoS requirements. Firstly, the Geographic Location Information Factors (GLIF) are defined to balance the network load by scheduling the ISLs with lower loads. Then, the optimization objective function is constructed by considering delay, remaining bandwidth, packet loss rate, and GLIF in order to characterize the routing problems caused by multi-service and load balancing. Following this, we propose an MSR-GLB algorithm that includes the state transition rule and the pheromone update rule. Among them, the state transition rule is based on QoS information and link GLIF, and the pheromone update rule has the characteristics of positive and negative feedback mechanism. The simulation results show that the MSR-GLB algorithm can well meet the QoS requirements of different services, balance the network load compared to Cross-layer design and Ant-colony optimization based Load-balancing routing algorithm in LEO Satellite Network (CAL-LSN) and Multi-service On-demand Routing (MOR) algorithm.

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 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.

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 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.

Communication infrastructure in remote areas struggles to deliver stable, high-quality services for power systems. Low Earth Orbit (LEO) satellite networks offer an effective solution through their low latency and extensive coverage. Nevertheless, the high orbital velocity of LEO satellites combined with massive user access frequently leads to signaling congestion and degradation of service quality. To address these challenges, this paper proposes a LEO satellite handover strategy based on Quality of Service (QoS)-constrained K-Means clustering and Deep Q-Network (DQN) learning. The proposed framework first partitions users into groups via the K-Means algorithm and then imposes an intra-group QoS fairness constraint to refine clustering and designate a cluster head for each group. These cluster heads act as proxies that execute unified DQN-driven handover decisions on behalf of all group members, thereby enabling coordinated multi-user handover. Simulation results demonstrate that, compared with conventional handover schemes, the proposed strategy achieves an optimal balance between performance and signaling overhead, significantly enhances system scalability while ensuring long-term QoS gains, and provides an efficient solution for mobility management in future large-scale LEO satellite networks.

Mobility models for low Earth orbit (LEO) satellite networks have been researched and reported on in depth. However, most of the analyses discounted the significance of Earth rotation with respect to the LEO satellite speed of rotation and trajectory. In this paper, an approach to the problem of LEO satellite mobility model that takes into consideration the Earth rotation for LEO satellite networks is put forward. The proposed novel mobility model is based on realistic and accurate facts, and involves the use of elementary vector calculus. Analysis of the impact of Earth rotation on LEO satellite networks is provided using simulation results and arguments. The importance for such a model in the study and analysis of LEO satellite systems is also discussed.

Recent research indicates that self-similarity is a common phenomenon in wired network. However, there are no sufficient studies that focus on the characteristics in wireless network, especially in low earth orbit (LEO) satellite network. This paper presents a practicable model based on analysis of aggregate self-similar traffic and impact on performances of LEO satellite network, and then validate that the traffic in LEO satellite network is self-similar. Then, the quality of service (QoS) measures under self-similar traffic such as queue length and buffer size are discussed.

Low earth orbit (LEO) satellite networks are capable of providing wireless connectivity to any part of the world while guaranteeing short propagation delays. There is a huge need for developing Internet protocol (IP) friendly networking technologies that aim to integrate emerging LEO satellite networks with the already existing terrestrial IP networks. LEO satellite networks are well characterized by frequent handover occurrences. These handovers largely affect mobility management in LEO satellite networks. Existing IP mobility management protocols, such as mobile IP, manage the location of mobile nodes on the basis of the network topology. Applying such mechanisms in LEO satellite networks will cause a binding update of mobile nodes upon every handover occurrence. Given the frequent occurrence of handovers in LEO satellite networks, a potentially large number of binding update requests will be generated and ultimately affects the scalability of mobility management. This paper argues a handover-independent mobility management scheme for LEO satellite networks. The proposed scheme purposes to exploit geographical location information to make the mobility management independent from handovers. This handover-independent management method reduces the number of update requests and eventually increases the system scalability. A detailed description of the actual implementation of the scheme is given. Through a mathematical analysis, the paper evaluates the required management cost and accordingly verifies the scalability of the proposed scheme.

One of the unique traffic features of low earth orbit (LEO) satellite networks is nonuniform traffic load distribution. Moreover, the traffic distribution is time-variant since each individual satellite moves along its orbit plane and the earth rotates on its axis simultaneously. A call in a LEO satellite network has to be rerouted periodically as a result of the satellite handover. In this paper, we propose an adaptive routing protocol in LEO satellite networks to provide better quality of service regardless the traffic distribution in the network. The proposing routing protocol distributes the traffic load of congested areas, while it optimizes the suboptimally routed path without the overhead of end-to-end rerouting. In addition, it reduces the handover blocking probability using a different threshold value for the call admission of handover calls.

Reinforcement learning based dynamic distributed routing scheme for mega LEO satellite networks

Frequent spotbeam handovers in low earth orbit (LEO) satellite networks require a technique to decrease the handover blocking probabilities. A large variety of schemes have been proposed to achieve this goal in terrestrial mobile cellular networks. Most of them focus on the notion of prioritized channel allocation algorithms. However, these schemes cannot provide the connection-level quality of service (QoS) guarantees. Due to the scarcity of resources in LEO satellite networks, a connection admission control (CAC) technique becomes important to achieve this connection-level QoS for the spotbeam handovers. In this paper, a geographical connection admission control (GCAC) algorithm is introduced, which estimates the future handover blocking performance of a new call attempt based on the user location database, in order to decrease the handover blocking. Also, for its channel allocation scheme, an adaptive dynamic channel allocation (ADCA) scheme is introduced. By simulation, it is shown that the proposed GCAC with ADCA scheme guarantees the handover blocking probability to a predefined target level of QoS. Since GCAC algorithm utilizes the user location information, performance evaluation indicates that the quality of service (QoS) is also guaranteed in the non-uniform traffic pattern.

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.

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.