Research on New Methods of LEO Satellite Networks Routing

Satellite Communication Networks System Systems for satellite communication networks include the technological set-ups and supporting infrastructure used for the transmission & reception of voice, video, and data signals. By utilising satellite technology to help with the transmission of information across several Earth locations, these networks enable long-distance communication. Ground stations, satellite systems, & user terminals are the main components of satellite communication networks. The importance of communication via satellite network structures in research cuts across several fields, underscoring the critical role they play. These systems play a crucial role in creating global connectedness and facilitating communication, highlighting the importance of this area of study. Satellite communication networks have a number of advantages, including the ability to connect to remote or inaccessible areas, abundant bandwidth capacity, and global coverage. They have several uses, including in remote sensing, disaster management, internet connectivity, broadcasting, and telecommunications. The Additional Ratio rating approach is a decision-making process used to evaluate and rank options by taking into account a number of factors. It comprises giving each criterion a weight and assessing how well the alternatives perform in comparison to a reference option. The ARAS technique calculates an overall score over each alternative by combining these weighted performance ratios. The choice with the greatest score is considered to be the best one. In situations where several criteria are present, the ARAS technique provides decision-makers with a systematic framework for efficiently evaluating and comparing alternatives. Laser communication, optical networks, satellite optical communication, vibrations, satellite networks Solar Radiation Pressure, Thermal Bending, Solar and Lunar Gravity, Micro Meteorite impacts, Earth oblations effects Through the rank table, we can get the rank of alternative parameters. Whereas satellite optical communication is in 1st position and satellite networks is in 5th position. First ranking satellite optical communication is obtained with the lowest quality of satellite networks.

With considering satellite networks' characteristic of few network nodes, limited on-board storage and processing ability etc., a distributed delay-constrained routing algorithm (DDCRA) with mechanism of probe packet is proposed. Based upon strategy of distributed routing and quality of service (QoS), DDCRA reduces the complexity of algorithm so as to lower requirements for the on-board processing equipments. Comparing with the minimum delay routing algorithm, better performances of DDCRA can be achieved.

The performance of satellite networks largely depends on their routing algorithm. Existing routing algorithms for satellite networks usually are not quite adaptive or have the disadvantage of a large consumption in both computation and storage resources. In this paper, we propose a novel routing algorithm based on dynamic clustering for LEO satellite networks. By dynamic forming cluster of the satellite network, the routing algorithm is both adaptive, computational effective and need much less storage space. Simulation results show that our routing algorithm performs better and are more robust when the network topology changes.

Low-orbit satellite communication networks have gradually become the research focus of fifth-generation (5G) beyond and sixth generation (6G) networks due to their advantages of wide coverage, large communication capacity, and low terrain influence. However, the low earth orbit mega satellite network (LEO-MSN) also has difficulty in constructing stable traffic transmission paths, network load imbalance and congestion due to the large scale of network nodes, a highly complex topology, and uneven distribution of traffic flow in time and space. In the service-based architecture proposed by 3GPP, the introduction of service function chain (SFC) constraints exacerbates these challenges. Therefore, in this paper, we propose GDRL-SFCR, an end-to-end routing decision method based on graph neural network (GNN) and deep reinforcement learning (DRL) which jointly optimize the end-to-end transmission delay and network load balancing under SFC constraints. Specifically, this method constructs the system model based on the latest NTN low-orbit satellite network end-to-end transmission architecture, taking into account the SFC constraints, transmission delays, and network node loads in the end-to-end traffic transmission, uses a GNN to extract node attributes and dynamic topology features, and uses the DRL method to design specific reward functions to train the model to learn routing policies that satisfy the SFC constraints. The simulation results demonstrate that, compared with graph theory-based methods and reinforcement learning-based methods, GDRL-SFCR can reduce the end-to-end traffic transmission delay by more than 11.3%, reduce the average network load by more than 14.1%, and increase the traffic access success rate and network capacity by more than 19.1% and two times, respectively.

This paper extends previous research efforts related to the simulation performance modelling and analysis of satellite communication networks. Specifically, the use of low earth orbit (LEO) satellite networks for personal communications is examined. Six different satellite constellation configurations are investigated in a packet-switched operating environment. Performance metrics examined are the end-to-end packet delay and the utilization of satellite channels in the dynamic environment. Realistic and accurate models of the physical satellite network and its terrestrial transmitters require that numerous operating characteristics and assumptions be specified. These are based on proposed design requirements of commercial systems, such as Iridium. Via the use of simulation, we show the relative delay and utilization performance of differing satellite network architectures. From these simulation models, mathematical metamodels are derived for the system delays. These innovative models are used to predict the delay performance of different network architectures not previously simulated. Comparison of these metamodels with simulation results show that metamodels provide an accurate means for performance prediction. Copyright © 1999 John Wiley & Sons, Ltd.

The integrated information network is a large capacity information network that integrates various communication platforms on the ground, at sea, in the air and in the deep air through the inter-satellite and satellite-ground links to acquire information accurately, process it quickly, and transmit it efficiently. The satellite communication, as an important part of integrated information networks, is one of main approaches to acquire, process and distribute communication information and resources. In this paper, based on current researches of the satellite communication network, we put forward a 3-layer satellite communication network model based on the Software Defined Network (SDN). Meanwhile, to improve current routing policies of the Low Earth Orbit (LEO) satellite communication network, we put forward an Adaptive Routing Algorithm (ARA) to sustain the shortest satellite communication link. Experiment results show that the proposed method can effectively reduce link distance and communication delay, and realize adaptive path planning.

Satellite networks are one of the most emergent areas in commercial communication. Today about twenty serious consortiums (such as Iridium, Teledesic, Globstar, and Celestri) develop satellite communication networks. In the new generation of satellite networks, laser will be used as a communication link between the satellites in order to get wider bandwidth for the network backbone. One of the main problems of a satellite laser link is dramatic reduction of the communication performances for up to several milliseconds due to deviation of the pointing direction of the very narrow laser beam. The satellite mechanical vibrations cause this deviation. This fact is an obstacle in implementation of high QoS (quality of service) application in satellite network. In this research we propose the use of the diversity concept in order to overcome the vibration effect. A communication diversity means using a number of independent propagation paths for transmitting the same information. This concept increases the probability of receiving the information. We derive the diversity algorithm based on standard ATM (synchronic transfer mode). A practical satellite communication network performance based on our algorithm is presented.

The transmission of huge amounts of data generated from various sensors is the basis and prerequisite for the analysis and prediction of the marine ecosystem. The special geographical conditions of the ocean determine that its data transmission is different from the common terrestrial data transmission. In this study, a marine environmental monitoring network architecture is proposed to collect environmental data from marine ecosystems using a heterogeneous network constructed by a multi-hop network and a satellite network. Considering the characteristics of satellite networks, such as high bandwidth delay product (BDP), long latency and high bit error rate, this work proposes a transmission scheme that improves the transmission performance of Transmission Control Protocol/Internet Protocol (TCP/IP)-based Internet networks over satellite networks. The improved scheme increases the monitoring data transmission rate, distinguishes between random data loss and congested data loss, rationalizes the threshold in fast recovery, and improves the overall transmission performance of the marine environmental monitoring network. The experimental results show that the proposed scheme can greatly improve the transmission performance of the monitoring network compared with traditional transmission protocols. The network model proposed in this study makes full use of current advanced network technologies, including multi-hop networks, wireless sensor networks, and satellite networks to expand the monitoring area at sea. This study provides a useful reference for the network model and data transmission for ocean monitoring.

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.

Making a satellite system a fully functional Internet router has challenges because the characteristics of satellite communication networks are mismatched to most common Internet link technologies in several important ways. Under some error conditions, "multiple segment losses within a single window" happens frequently and traditional selective acknowledgment (SACK) implementation may force the TCP sender to retransmit packets that have already been received successfully by the receiver. This paper puts forward a proposed SACK+ implementation to prevent unwanted retransmission in satellite communication networks. On the other hand, in order to provide a fair bandwidth allocation to each of n flows that share the outgoing link of a congested router and to overcome low efficiency when the number of high-volume sources is small, we propose CHOKe+ (choose and keep for responsive flows, choose and kill for unresponsive flows+) as the fair queuing policy in satellite communication networks. We show that the proposed implementation of SACK+/CHOKe+ makes a good tradeoff between "efficiency" and "fairness" by NS2 simulations.

Uneven traffic demand in satellite networks can lead to link congestion and influence network communication quality. To tackle this problem, we propose a state‐aware routing algorithm based on traffic prediction to disperse network traffic. Reducing the onboard processing requirements, the routing algorithm in this paper introduces a software‐defined network to decouple the control plane and data plane of the satellite network. Also, to better sense the trend of network state, the routing algorithm devises a Spatial‐Temporal Attention Fusion Graph Neural Network (STAFGNN) to predict the future network state by capturing the spatial‐temporal correlation of satellite network traffic. In the control plane, the routing algorithm senses the network state by predicting the future satellite traffic and aggregates the real‐time states of neighboring satellites to identify the load level of the zone. The data plane forwards data based on the control information. Based on the routing algorithm in this paper, multipath routing is designed to reduce the probability of link congestion by dispersing each end‐to‐end traffic. The simulation results demonstrate that the routing algorithm in this paper reduces the number of congestion links and balances the load distribution. The proposed algorithm provides an idea to introduce deep learning to solve the load balancing problem in satellite networks.

Satellite communication networks have been widely used to provide essential communication services, including voice communication, global positioning, message communication, etc. However, sorts of network attacks are easy to be launched in these networks due to the limited computation capability and communication width, long communication delay, and intermittent link connection. In this paper, we first propose a new [E]ncryption-based [M]utual [A]uthentication and [K]ey [U]pdate (EMAKU) protocol in satellite communication networks. Next we analyze the security of the EMAKU protocol under two classic network attacks which are replay attack and man-in-the-middle attack. Finally, experiments show that the EMAKU protocol is 21.5% faster than the traditional encryption-based authentication protocols, and the average time of key update of the EMAKU protocol is about 450.01 ms.

Satellite networks have many advantages over traditional terrestrial networks. However, it is very difficult to design a satellite network with excellent performance. The paper briefly summarizes some existing satellite network routing technologies from the perspective of both single-layer and multilayer satellite constellations, and focuses on the main ideas, characteristics, and existing problems of these routing technologies. For single-layer satellite networks, two routing strategies are discussed, virtual node strategy and virtual topology strategy. Moreover, considering the deficiency of existing multilayer satellite network routing, we discuss the topic invulnerability. Finally, the challenges and problems faced by the satellite network are analyzed and the trend of future development is predicted.

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.

Several IP-based routing algorithms have been developed for low-Earth orbit (LEO) satellite networks in recent years. The performance of the satellite IP networks can be improved drastically if multiple satellite constellations are used in the architecture. In this work, a multilayered satellite IP network is introduced that consists of LEO, medium-Earth orbit (MEO), and geostationary Earth orbit (GEO) satellites. A new multilayered satellite routing algorithm (MLSR) is developed that calculates routing tables efficiently using the collected delay measurements. The performance of the multilayered satellite network and MLSR is evaluated through simulations and analysis.