The Solution of Packet Reordering in LEO Satellite Networks

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.

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

Current low earth orbit (LEO) satellite network display poor power efficiency, running network devices at full capacity all the time regardless of the traffic matrix and the distribution of the population over the Globe. Most of the research on energy efficiency of LEO satellites has focused on component level or link level. Therefore, this kind of research is not holistic to try to look at the satellite system as a single node. To enhance the energy efficiency. The solution should exploits multipath routing and load balancing. LEO network is overprovisioned, and hence selectively shutting down some satellite nodes and links during off-peaks hours seems like a good way to reduce energy consumption. In this paper, we exploit the fact that due to geographical and climatic conditions, some satellite links are expected to be loaded with data while others remain unused. Our approach is to power down satellite nodes and links during period of low traffic, while guaranteeing the connectivity and QoS. Finding the optimal solution is NP-problem and therefore, we explore in this work two heuristic algorithms. We evaluate our heuristics on a realistic LEO topology and real traffic matrices. Simulation results show that the power saving can be significant.

We propose a routing methodology for multi-hop low Earth orbit (LEO) satellite networks aiming at minimising the number of link handovers per connection, while at the same time satisfying QoS requirements. For the representation of the network topology, an approach with a graph method is used, where a new metric for inter-satellite links, called lifetime, is introduced. This metric is then considered for minimisation of connection re-routing attempts due to the connectivity changes. In addition to this, we propose a QoS-based routing strategy for multi-service LEO satellite networks, for which the so-called per service performance is studied by numerical examples.

Recently, more research attention has been focused on LEO (Low Earth Orbit) satellite networks connected to ISL (Inter Satellite Link). If terrestrial and LEO satellite networks are seamlessly interconnected by TCP/IP protocols, a global network that covers the whole earth can be integrated into the Internet. However, handover and rerouting issues in LEO satellite networks cause delay variance, and the delay variance can degrade performance of TCP, originally designed for wired networks, because of unnecessary retransmission. In this paper, we propose a method for avoiding the unnecessary retransmission by using the TTL (Time To Live) field in IP headers. Our simulation results show how our mechanism is effectively avoiding unnecessary retransmissions. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 88(1): 64–71, 2005; Published online in Wiley InterScience (www.interscience.wiley. com). DOI 10.1002/ecja.10205

The low earth orbit (LEO) satellite network has the potential to provide low-latency transmission for global Internet services. Due to the mobility of LEO satellites, the frequent handover between a ground station (GS) and satellites will significantly affect the quality of content delivery. To overcome this challenge, we propose a multicast source routing mechanism for large-scale LEO satellite networks. This mechanism enables the source satellite to flexibly select the routes towards multiple satellites covering the same GS, thus ensures seamless satellite handover. Different from IP-based multicast, our approach assigns link identifiers for each unidirectional inter-satellite link, which can enable us to achieve reliable and efficient packet forwarding. Specifically, we design the equivalent virtual links based on the link identifiers, which can mitigate the impact of occasional link failures by forwarding the packets via the alternative route. Moreover, we use two bloom filters to encode the route (i.e., physical and virtual link identifiers) into the packet header. This way, the intermediate satellites could efficiently forward the packet according to its link identifier table. We evaluate the performance of the proposed mechanism via packet-level simulation on OMNeT++. Results show that our proposed method outperforms IP-based routing in terms of end-to-end delay and delivery ratio.

For those satellites in low earth orbit(LEO) satellite networks, due to limited storage capacity and other factors, an important task is to download their data collected in the space to earth stations (ESs). To maximize the throughput of data downloading from satellite to ESs, thus far, the existing research work has extensively studied how to efficiently allocate download time of ESs to satellites. However, a considerable part of satellites have barely contact time with ESs, because of the uneven contact between satellites and ESs.In this paper, we aim at optimizing data downloading from satellites to multiple ESs by using our inter-satellite link(ISL) routing method to offload data from the satellites that have surplus data to the satellites that have surplus contact time with ESs. First the entire satellite operation period is divided into several time intervals, a time interval is a time unit of data transmission. Then we allocate the download time between satellites and ESs. Lastly, the topology of satellites is modeled as Twisted Manhattan topology and our offloading algorithm is proposed to offload data among the satellites. Extensive simulations have been conducted to evaluate the effectiveness of our proposed scheme. The simulation results show that our scheme can significantly increase the overall system throughput of data downloading.

Thanks to their wide coverage and relatively low latency compared to geosynchronous satellites, Low Earth Orbit (LEO) satellite networks have been regarded as one of the most promising solutions to provide global broadband backhaul for mobile users and IoT devices. In LEO satellite networks, the frequent feeder link handover invokes unacceptable communication interruptions and affects the backhaul quality. To overcome this challenge, we propose a maximum backhaul capacity handover strategy for feeder links in LEO satellite networks. To improve the backhaul capacity, we design an available backhaul capacity ratio to jointly consider feeder link quality and the inter-satellite network in handover decisions. In addition, we introduce a service time factor and handover control factor to reduce the handover frequency. Then, we propose the handover utility function based on the designed handover factors and propose a greedy-based handover strategy. Simulation results show that the proposed strategy outperforms conventional handover strategies in backhaul capacity with low handover frequency.

Low Earth Orbit (LEO) satellite networks are playing an important role in current global mobile communications. An optimized handover scheme can greatly improve performance in respect of end-to-end communication delay and link bandwidth utilization of LEO satellite networks. Various handover schemes in LEO satellite networks are researched in this paper. Ground-satellite link handover and recomputing routes problem is analyzed in detail. Then, a link handover algorithm for LEO satellite mobile communication network is proposed. Simulation results show that the satellite link handover algorithm proposed has less end-to-end delay, and is more stable and customizable.KeywordsSatellite NetworkHandover SchemeDynamic Channel AllocationHandover ManagementHandover AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

As an integral component of the space-air-ground integrated network (SAGIN), the low Earth orbit (LEO) satellite network has displayed immense potential in providing ubiquitous connectivity and broadband mobile communication. However, the intrinsic dynamics of LEO satellites pose unprecedented challenges in network management and service delivery. In this paper, we investigate the service function chain (SFC) orchestration in dynamic LEO satellite networks to achieve flexible and efficient service provision. Considering the service requirements and the limitations of network resources, we formulate the SFC orchestration problem as the integer nonlinear programming (INLP) problem for maximizing the service acceptance and the load fairness of satellites. Then, an efficient heuristic algorithm is proposed to solve this problem. Addressing the situation with frequent service requests, a graph attention network (GAT)-based approach with low complexity is also presented. Simulation results demonstrate that our proposed approaches outperform the benchmarks by a substantial margin in terms of load fairness and service acceptance. Besides, the proposed GAT-based approach shows its advantage in computation complexity, and exhibits robustness in unstable network scenarios with intermittent link interruptions.

Aiming at the problems of high dynamic topology and limited on-board resources in Low Earth Orbit (LEO) satellite networks leading to poor performance of traditional routing algorithms, this paper combines the centralized control idea of Software-Defined Networking (SDN) with the collective learning ability of the Particle Swarm Optimization (PSO) algorithm to propose an adaptive potential field routing algorithm based on PSO. This algorithm models the network as a dynamic potential field, defines potential energy based on the cumulative congestion degree to guide data packet transmission; adaptively learns and optimizes link cost weights through the PSO mechanism, achieving dynamic evolution of the path evaluation criterion; and adopts a probability-based multi-path forwarding mechanism to achieve load balancing. Simulation results show that compared to traditional algorithms such as Dijkstra and ACO (Ant Colony Optimization), this algorithm significantly improves performance in terms of average end-to-end delay, network throughput, and load balancing, especially suitable for dynamic traffic scenarios.

A fair and adaptive call admission control algorithm for multimedia low Earth orbit (LEO) satellite networks was proposed. Based on current call dropping probability of destination cell, this algorithm reserves bandwidth for handoff calls using double threshold method. To avoid the discrimination of quality of service (QoS) caused by the allocation based on fair bandwidth, this algorithm adopts the bandwidth allocation rule based on fair QoS. Simulation results show that the proposed algorithm can accurately and adaptively reserve bandwidth, present satisfactory call blocking probability and greatly reduce handoff call dropping probability, while guarantees the high bandwidth utilization.

Low Earth Orbit (LEO) satellite networks are an important part of the global communication infrastructure today. Despite ongoing efforts to improve their resilience, they remain vulnerable to component damage and deorbiting under harsh space weather conditions. Prior work identified a modest but noticeable impact on LEO satellite network performance during solar storms, typically manifesting as an immediate rise in packet loss and a sustained increase in round-trip time (RTT). However, these studies offer only coarse-grained insights and do not capture the nuanced spatial and temporal patterns of disruption across the LEO network.

The rapid expansion of low Earth orbit (LEO) satellite networks to improve global internet access has raised concerns about their environmental impact compared to terrestrial infrastructure. This study conducts a panel lifecycle assessment (LCA) and econometric analysis to compare the carbon intensity of LEO satellite and terrestrial fibre-optic networks across 100 countries from 2005 to 2023. Using ISO 14040-compliant LCA, we find LEO systems emit 0.1–0.5 kg CO₂e/GB, significantly higher than terrestrial systems (0.05–0.20 kg CO₂e/GB), with these emissions generated primarily during the launch (40%) and operational (35%) phases. Fixed-effects regression reveals that internet usage increases CO₂ emissions per capita (β = 0.1435, p < 0.01), while the share of renewable energy reduces emissions (β = –95.1288, p < 0.05), with LEO penetration having only a minimal impact (β = 0.0012, p = 0.332). Advanced two-stage least squares, Arellano-Bond generalized method of moments, Moran’s I (I = 0.12, p = 0.08), and random forest analyses confirm these findings, highlighting renewables and internet usage as key drivers of emissions. The findings suggest that emissions reporting frameworks and renewable energy integration may play an important role in aligning digital connectivity expansion with climate goals. These results highlight the need for sustainable digital infrastructure to balance global connectivity with planetary boundaries.

For developing the ubiquitous infrastructures of pervasive computing, low earth orbit (LEO) satellite networks, with the ability to provide global broadband access service, are considered as the complement and extension of the terrestrial networks. Due to the dynamic topology and the non-uniform distribution of terrestrial users, delivery time within LEO satellite networks is liable to fluctuate, which might deteriorate the capacity of accessing computing resource for users. Thus, to investigate the time constraint, a delivery time computational model for LEO satellite networks is proposed. With source inputs of Markov-modulated Poisson process (MMPP) and a hot-spot distribution of destinations, a tandem queue for the target flow is established with cross-flows. Then departure interval moments of the target flow are calculated and fitted for the next link as input parameters, from which the queuing delay under cross-flows is iteratively obtained. The comparison between computational results and simulation results demonstrates that this model makes a good depiction of the influence by the traffic pattern with satisfying accuracy.