Satellite Network Routing Research Articles

Collaborative last-hop scheduling strategy for large-scale LEO constellation routing

The transmission of data packets from satellites to Earth Stations (ESs) is the last hop of satellite network routing. As the final stage of packet transmission, the use of different scheduling strategies directly affects the throughput of the satellite network. Traditionally, researchers have attempted to enhance network performance by investigating inter-satellite routing protocols or inter-satellite data offloading strategies. However, these approaches have failed to address scheduling issues in the last hop of large-scale Low Earth Orbit (LEO) constellations. In this paper, we present the first Cooperative Last-Hop Scheduling (CLHS) strategy for routing in large-scale LEO constellations based on a bidirectional communication domain. In this strategy, we first utilize the bidirectional communication domain to determine the communication ranges of satellites and ESs. Subsequently, an information flow is established to interact with ESs and satellites. The ESs receive and reconstruct the Information Matrix (IM) from the information flow. Moreover, we propose the Maximum Decision Value Priority (MDVP) algorithm, which takes the reconstructed IM as input and computes the scheduling commands for satellites within the communication range of the ESs. To address the issue of multiple ESs simultaneously scheduling the same satellite, we introduce the Collision Avoidance Algorithm (CAA). Finally, to enhance the data packet transmission efficiency of the scheduled satellites, we propose the Weighted First-In-First-Out (WFIFO) algorithm, which is specifically designed for satellite packet dequeuing. We validate the CLHS through simulations on two satellite constellations: the first-generation Starlink constellation with 4409 satellites and the GW-2 constellation with 6912 satellites. The results show that CLHS can achieve better network throughput than traditional strategies. CLHS provides a novel method of scheduling the last hop in large-scale satellite constellations.

Link attributes based multi-service routing for software-defined satellite networks

As the potential complement to terrestrial networks, satellite networks are expected to facilitate full coverage and broadband access at anytime and anywhere. However, satellite networks are characterized by highly dynamic topology and load, and how to design an adaptive routing algorithm to meet diverse application needs faces significant challenges. In this paper, we propose a Link-Attributes-based multi-service On-Demand Routing (LAODR) algorithm to provide highly reliable and service-aware data forwarding for Software -Defined Satellite Networks (SDSN). Specifically, we construct a quantitative model of link reliability which provides a fine-grained state description of the dynamic topology. Furthermore, we select the K-shortest path as the solution space and plan routing paths based on NSGA-II to allocate link resources reasonably. Extensive experiments on the Iridium validate that LAODR outperforms state-of-art routing algorithms in terms of end-to-end average latency, packet loss, throughput and node congestion degree, as well as meets the requirements of diverse services.

Application of an improved simulated annealing algorithm in satellite network routing

Abstract Satellite communication is integral to modern communication systems, yet navigating the intricacies of routing in networks characterized by high loads and dynamic evolution poses a significant challenge. This research is dedicated to enhancing satellite communication routing algorithms, with the goal of bolstering network efficiency and amplifying the success rate of business transmissions. Our approach introduces an improved simulated annealing algorithm incorporating adaptive temperature control and innovative path neighborhood exploration strategies. This novel algorithm is adept at adjusting to ever-shifting network topologies and meeting various business requirements. Our simulation results reveal that this refined algorithm outperforms traditional annealing methods in several critical aspects. It elevates the success rate of business transmissions, reduces the average number of forwardings by 6.546% across various load scenarios, and diminishes system response time by 15.05%, thereby marking a substantial improvement in network performance.

Galor: Global view assisted localized fine-grained routing for LEO satellite networks

Low Earth Orbit (LEO) satellite networks have been expected to provide global coverage for Internet services with immediacy requirements. The dynamics of an LEO satellite network topology induce the challenges of achieving efficient content retrieval. This article takes an initial step toward achieving efficient content retrieval in LEO satellite networks from the routing perspective. We start with investigating the topology characteristics of LEO satellite networks in terms of the deterministic neighbor relation and the intermittent inter-satellite links. We then propose a Global view Assisted Localized fine-grained Routing (GALOR), which is an information-centric routing mechanism customized to LEO satellite networks. Specifically, GALOR disseminates the link state within a predefined range instead of the entire constellation, incurring less convergence time and control overhead. Therefore, GALOR can calculate the routing table (to guide interest forwarding) based on the local link state and the global neighbor relations. Moreover, GALOR improves the forwarding method of the information-centric routing by reconstructing a failed Pending Interest Table (PIT) entries in response to occasional link failures. Our packet-level experiments show that GALOR outperforms state-of-the-art mechanisms (up to 103.4%) in terms of average packet delivery ratio in content-sharing.

A method for analyzing the impact of SEUs on satellite networks from the perspective of distributed routing

Single event upset (SEU) refers to the change in the state of a digital circuit caused by a single, energetic particle passing through the sensitive area of semiconductor components. In the space environment, cosmic rays and high-energy particles may lead to SEUs, which can cause failures of routing functions, such as forwarding errors, in satellite networks. This paper focuses on the impact of SEUs on satellite networks from the perspective of distributed routing. The influence of SEUs on a single satellite can easily spread to the entire satellite network through distributed routing, which can influence the reliability of the satellite network. We propose a framework for the reliability analysis of satellite networks under the impact of SEUs. According to the influence of SEUs on the execution of routing programs in different routing phases, we present the error propagation model of satellite networks for understanding SEUs in satellite network routing. We found that all the influences on program execution ultimately converge on data forwarding and can be reflected in the proposed path topology graph. Therefore, we propose an algorithm that can evaluate the impact of SEUs on satellite networks by analyzing the path topology graphs in both normal and SEU scenarios. The feasibility and effectiveness of the proposed method is demonstrated by experiments. The proposed method could provide a new perspective and theoretical basis for research on secure routing mechanisms and program/instruction reinforcement.

Low Earth Orbit Satellite Network Routing Algorithm Based on Graph Neural Networks and Deep Q-Network

Low Earth orbit (LEO) satellite networks are characterized by rapid topological changes, numerous network nodes and varying states of node resource constraints, which have resulted in traditional routing algorithms no longer being suitable for LEO satellite network routing. Therefore, this paper proposes an inductive learning architecture based on Graph Sample and Aggregate (GraphSAGE), which can significantly reduce the number of topology nodes to be trained, thereby reducing the computational complexity of the nodes. Then deep reinforcement learning (DRL) is employed for the continuous learning optimization of routing algorithms, and its generalization is improved by selecting GraphSAGE to construct the DRL agent. In the proposed graph neural-network-based routing optimization algorithm for LEO satellite networks, each Deep Q-Network (DQN) agent independently generates the hidden states of the nodes through the GraphSAGE model and uses them as inputs to the DRL model to make routing decisions. After a simulation and comparison, the proposed algorithm not only improves the overall network throughput, but also reduces the average end-to-end delay. The average throughput of the proposed algorithm increases by 29.47% and 18.42% compared to that of Dijkstra and the DQN, respectively. The average end-to-end delay is reduced by 39.76% and 15.29%, respectively, and can also adapt to changing topologies.

FPGA-based Inference Parallelization for Onboard RL-based Routing in Dynamic LEO Satellite Networks

FPGA-based Inference Parallelization for Onboard RL-based Routing in Dynamic LEO Satellite Networks

SENDIT: a satellite network routing digital twin solution for mega-constellations

SENDIT: a satellite network routing digital twin solution for mega-constellations

Reliability Analysis of Multi-Hop Routing in Multi-Tier LEO Satellite Networks

Reliability Analysis of Multi-Hop Routing in Multi-Tier LEO Satellite Networks

Swarm-Intelligence-Based Routing and Wavelength Assignment in Optical Satellite Networks

Swarm-Intelligence-Based Routing and Wavelength Assignment in Optical Satellite Networks

Research on Hopping Routing of Periodic Multiorbit LEO Satellites

In recent years, the low Earth orbit microsatellite network technology has experienced rapid development due to its advantages of low latency, low cost, and short development cycles. However, building an efficient and reliable satellite network routing system faces many challenges due to the characteristics of satellite networks, such as fast‐changing topology, large variations in link latency, higher probabilities of node and link failures, and limited resources. Routing algorithms have a significant impact on intersatellite communication and have become a research hotspot. The current mainstream algorithms focus on reducing information propagation delays between satellites to enable faster transmission. However, many satellite networks, such as meteorological observation satellite networks, are not sensitive to propagation delays but emphasize reducing hardware costs, especially for the receiving and transmitting signal systems of satellites. This means minimizing the single‐step signal transmission distance of satellites. This article proposes a routing algorithm based on time slot planning and the shortest path step length. Experimental simulation results demonstrate that this algorithm significantly reduces the step length of signal transmission and lowers hardware costs.

Traffic-Predictive Routing Strategy for Satellite Networks

To address the issues of uneven satellite network load and unstable link connections, a globally adaptive satellite network routing strategy based on traffic prediction (G-AODV) is proposed by enhancing the existing Ad hoc On-Demand Distance Vector Routing (AODV) protocol. To prevent heavily loaded nodes from becoming intermediate nodes, G-AODV introduces a traffic prediction mechanism in the route discovery phase. The routing request packet adopts the corresponding forwarding control policy based on the comparison between the predicted traffic load value at the next moment and the dynamic threshold. At the same time, a path replacement strategy is adopted to replace paths before node congestion occurs to achieve load balancing. Considering the unstable characteristics of the satellite chain that easily breaks, the route maintenance phase selects a path repair method by judging the node stability to avoid secondary breaks in the route. The simulation results show that in scenarios with different packet delivery rates, compared with the three comparative routing strategies, the packet delivery rate of G-AODV is increased by up to 20%, the packet loss rate is reduced by 22%, and the end-to-end delay is significantly reduced. In scenarios with different communication connection pairs, G-AODV’s packet delivery rate increased by up to 20%, the packet loss rate decreased by 18%, and the end-to-end delay was still reduced.

Satellite Network Security Routing Technology Based on Deep Learning and Trust Management.

The conventional trust model employed in satellite network security routing algorithms exhibits limited accuracy in detecting malicious nodes and lacks adaptability when confronted with unknown attacks. To address this challenge, this paper introduces a secure satellite network routing technology founded on deep learning and trust management. The approach embraces the concept of distributed trust management, resulting in all satellite nodes in this paper being equipped with trust management and anomaly detection modules for assessing the security of neighboring nodes. In a more detailed breakdown, this technology commences by preprocessing the communication behavior of satellite network nodes using D-S evidence theory, effectively mitigating interference factors encountered during the training of VAE modules. Following this preprocessing step, the trust vector, which has undergone prior processing, is input into the VAE module. Once the VAE module's training is completed, the satellite network can assess safety factors by employing the safety module during the collection of trust evidence. Ultimately, these security factors can be integrated with the pheromone component within the ant colony algorithm to guide the ants in discovering pathways. Simulation results substantiate that the proposed satellite network secure routing algorithm effectively counters the impact of malicious nodes on data transmission within the network. When compared to the traditional trust management model of satellite network secure routing algorithms, the algorithm demonstrates enhancements in average end-to-end delay, packet loss rate, and throughput.

Knowledge Graph Aided Network Representation and Routing Algorithm for LEO Satellite Networks

The compelling applications of Low earth orbit (LEO) satellite networks in our daily lives have been witnessed in recent years, ranging from weather forecasts to military monitoring. LEO satellite networks have grown in importance as a complement to terrestrial networks aiming at delivering global, ubiquitous communication. However, due to the LEO network topology keeps changing dynamically, the development of efficient routing algorithms becomes one of the challenges in the LEO network. Traditional routing policies hardly solve the rerouting problem caused by link switchover and the high calculation cost caused by large-scale irregular satellite topology. In this paper, we propose a knowledge graph-aided representation of satellite network topologies and routing architecture to optimize path selection and calculation cost for lower packet loss ratio and average delay. Moreover, we generate a routing policy by predicting potential relations between data packets and nodes to select the best relay nodes with the maximum probability of forwarding relations. Finally, extensive simulations are performed to evaluate the performance and availability of our proposed algorithm.

Optical transmittance based store and forward routing in satellite networks

Quantum computing will play a crucial part in our security infrastructure for the coming years. Quantum networks can consist of direct optical fiber or free-space links. With the use of satellite channels, we can create a quantum network with higher coverage than using optical fibers where the distances are limited due to the properties of the fiber. One of the highest drivers of cost for satellite networks, apart from the cost of the technology needed for such systems, are the costs of launching and maintaining said satellites. By minimizing the satellites needed for a well-functioning quantum network, we can decrease said network’s cost, thus enabling a cheaper quantum internet. In this paper, we present an optical transmittance-based routing algorithm with which it is possible to conduct successful quantum entanglement transfer between terrestrial nodes.

Routing Heterogeneous Traffic in Delay-Tolerant Satellite Networks

Delay-tolerant networking (DTN) offers a novel architecture that can be used to enhance store-carry-forward routing in satellite networks. Since these networks can take advantage of scheduled contact plans, distributed algorithms like the Contact Graph Routing (CGR) can be utilized to optimize data delivery performance. However, despite the numerous improvements made to CGR, there is a lack of proposals to prioritize traffic with distinct quality of service (QoS) requirements. This study presents adaptations to CGR to improve QoS-compliant delivery ratio when transmitting traffic with different latency constraints, along with an integer linear programming optimization model that serves as a performance upper bound. The extensive results obtained by simulating different scenarios show that the proposed algorithms can effectively improve the delivery ratio and energy efficiency while meeting latency constraints.

Distributed and Distribution-Robust Meta Reinforcement Learning (D$^{2}$-RMRL) for Data Pre-Storage and Routing in Cube Satellite Networks

In this paper, the problem of data pre-storage and routing in dynamic, resource-constrained cube satellite networks is studied. In such a network, each cube satellite delivers requested data to user clusters under its coverage. A group of ground gateways will route and pre-store certain data to the satellites, such that the ground users can be directly served with the pre-stored data. This pre-storage and routing design problem is formulated as a decentralized Markov decision process (Dec-MDP) in which we seek to find the optimal strategy that maximizes the pre-store hit rate, i.e., the fraction of users being directly served with the pre-stored data. To obtain the optimal strategy, a distributed distribution-robust meta reinforcement learning (D <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -RMRL) algorithm is proposed that consists of three key ingredients: value-decomposition for achieving the global optimum in distributed setting with minimum communication overhead, meta learning to obtain the optimal initial to reduce the training time under dynamic conditions, and pre-training to further speed up the meta training procedure. Simulation results show that, using the proposed value decomposition and meta training techniques, the satellite networks can achieve a 31.8% improvement of the pre-store hits and a 40.7% improvement of the convergence speed, compared to a baseline reinforcement learning algorithm. Moreover, the use of the proposed pre-training mechanism helps to shorten the meta-learning procedure by up to 43.7%.

Spatial Location Aided Fully-Distributed Dynamic Routing for Large-Scale LEO Satellite Networks

Due to the explosive growth in the scale of Low Earth Orbit (LEO) satellite networks, designing dynamic routing has become a promising way to improving satellite communication performance. Most of the existing approaches derive routing policies in a centralized paradigm, which often suffer from the high cost of collecting global routing information and the high computational complexity in large-scale networks. Therefore, this letter proposes a spatial location aided fully distributed routing algorithm for large-scale satellite network with minimizing the average delivery time. Based on this, a novel Fully Distributed dynamic Routing algorithm based on Multi-Agent deep Reinforcement Learning (FDR-MARL) is proposed to derive the optimal routing strategy. Extensive experiments are carried out to verify the effectiveness and advantages of our proposed approach under large-scale LEO satellite networks.

Stochastic Geometry-Based Low Latency Routing in Massive LEO Satellite Networks

In this article, the routing in massive low earth orbit (LEO) satellite networks is studied. When the satellite-to-satellite communication distance is limited, we choose different relay satellites to minimize the latency in a constellation at a constant altitude. First, the global optimum solution is obtained in the ideal scenario when there are available satellites at all the ideal locations. Next, we propose a nearest neighbor search algorithm for realistic (nonideal) scenarios with a limited number of satellites. The proposed algorithm can approach the global optimum solution under an ideal scenario through a finite number of iterations and a tiny range of searches. Compared with other routing strategies, the proposed algorithm shows significant advantages in terms of latency. Furthermore, we provide two approximation techniques that can give tight lower and upper bounds for the latency of the proposed algorithm, respectively. Finally, the relationships between latency and constellation height, satellites' number, and communication distance are investigated.

Energy-efficient routing in LEO satellite networks for extending satellites lifetime

Low Earth Orbit (LEO) satellites, when exposed to the sun, use solar energy for operation, processing, and communication, and with excess energy they recharge their batteries. However, when satellites are in an area with no sunlight, called eclipse areas, they operate using only their battery power. The batteries have limitations on the amount of recharges/discharges, also known as the depth of discharge (DOD) cycle. Therefore, this restricts the useful life of the batteries themselves and also of the satellites. In this paper, we propose two different efficient routing methods for LEO satellite networks, which optimize traffic in order to reduce the DOD of satellites. We improved the Energy and Capacity Aware Routing (ECARS) metric, existing in the literature, by adding the Energy Routing prUning (ERU)-DOD and Energy Routing penAlty (ERA)-DOD methods. These proposed methods prune and penalize, respectively, the links whose satellites have reached a certain minimum battery charge threshold. With this procedure, we avoid over-discharging the satellites’ battery, and thus, the lifetime is extended. Simulations results show that ERU-DOD and ERA-DOD can increase 133.19% and 11.88% the satellites’ batteries lifetime, respectively. Moreover, when comparing our ERU-DOD and ERA-DOD proposals with the ECARS in terms of the average residual energy of the batteries, they provide an increase of 109.77% and 32.73%, respectively. In addition, ERU-DOD and ERA-DOD proposals showed a gain in throughput of 7.28% and 3.06% higher than ECARS. Furthermore, compared to the ECARS metric, the ERA-DOD proposal has a 1.1% lower delay, a 1.5% lower number of hops, and 0.49% less in the total of blocked sources.