Satellite Networks Research Articles

MOLM: Alleviating Congestion through Multi-Objective Simulated Annealing-Based Load Balancing Routing in LEO Satellite Networks

In satellite networks, existing congestion resolution methods do not consider the predictability and stability of paths, leading to frequent path switches and high maintenance costs. In this regard, we propose a novel congestion resolution approach, named MOLM, which introduces a continuous neighbor set during path updates. This set includes nodes capable of establishing sustainable connections with the predecessors and successors of congested nodes. Combined with a multi-objective simulated annealing framework, MOLM iteratively derives an optimal selection from this set to replace congested nodes. Additionally, we employ a Fast Reroute mechanism based on backup paths (FRR-BP) to address node failures. The simulation results indicate that the optimal node endows the new path with optimal path stability and path latency.

Narrowband Internet of Things via Low Earth Orbit Satellite Networks: An Efficient Coverage Enhancement Mechanism Based on Stochastic Geometry Approach.

With the development of IoT technology and 5G massive machine-type communication, the 3GPP standardization body considered as viable the integration of Narrowband Internet of Things (NB-IoT) in low Earth orbit (LEO) satellite-based architectures. However, the presence of the LEO satellite channel comes up with new challenges for the NB-IoT random access procedures and coverage enhancement mechanism. In this paper, an Adaptive Coverage Enhancement (ACE) method is proposed to meet the requirement of random access parameter configurations for diverse applications. Based on stochastic geometry theory, an expression of random access channel (RACH) success probability is derived for LEO satellite-based NB-IoT networks. On the basis of a power consumption model of the NB-IoT terminal, a multi-objective optimization problem is formulated to trade-off RACH success probability and power consumption. To solve this multi-objective optimization problem, we employ the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) method to obtain the Pareto-front solution set. According to different application requirements, we also design a random access parameter configuration method to minimize the power consumption under the constraints of RACH success probability requirements. Simulation results show that the maximum number of repetitions and back-off window size have a great influence on the system performance and their value ranges should be set within [4, 18] and [0, 2048]. The power consumption of coverage enhancement with ACE is about 58% lower than that of the 3GPP proposed model. All this research together provides good reference for the scale deployment of NB-IoT in LEO satellite networks.

Performance analysis of multi-hop low earth orbit satellite network over mixed RF/FSO links

Low earth orbit satellite (LEOS) communication has emerged as a promising technology to achieve global coverage, while further reducing the end-to-end latency by establishing communication link between LEOSs. In this paper, we analyze the performance of multi-hop LEOS network in terms of bit error rate (BER) and outage probability over mixed radio frequency (RF)/free space optical (FSO) links where RF link is used for the link between ground terminal and LEOS and FSO link is used for establishing inter-satellite links (ISLs). The derived closed-form results are confirmed via Monte Carlo simulations and we show the effect of the number of satellite hops on outage probability and average BER performance by comparing scenarios both with and without ISLs.

From 5G to 6G: revolutionizing satellite networks through TRANTOR foundation

From 5G to 6G: revolutionizing satellite networks through TRANTOR foundation

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

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

LEO Satellite Downlink Distributed Jamming Optimization Method Using a Non-Dominated Sorting Genetic Algorithm

Due to their low orbit, low-Earth-orbit (LEO) satellites possess advantages such as minimal transmission delay, low link loss, flexible deployment, diverse application scenarios, and low manufacturing costs. Moreover, by increasing the number of satellites, the system capacity can be enhanced, making them the core of future communication systems. However, there have been instances where malicious actors used LEO satellite communication equipment to illegally broadcast events in large sports stadiums or engage in unauthorized leakage of military secrets in sensitive military areas. This has become an urgent issue in the field of communication security. To combat and prevent abnormal and illegal communication activities using LEO satellites, this study proposes a LEO satellite downlink distributed jamming optimization method using a non-dominated sorting genetic algorithm. Firstly, a distributed jamming system model for the LEO satellite downlink is established. Then, using a non-dominated sorting genetic algorithm, the jamming parameters are optimized in the power, time, and frequency domains. Field jamming experiments were conducted in the southwest outskirts of Xi’an, China, targeting the LEO constellation of the China Satellite Network. The results indicate that under the condition that the jamming coverage rate is no less than 90%, the proposed method maximizes jamming power, minimizes time delay, and minimizes frequency compensation compared to existing jamming optimization methods, effectively improving the real-time jamming performance and success rate.

Routing networking technology based on improved ant colony algorithm in space-air-ground integrated network

Space-air-ground integrated networks comprise a multi-level heterogeneous integrated network that combines satellite-based, aerial, and terrestrial networks. With the increasing human exploration of space and growing demands for internet applications, space-air-ground integrated networks have gradually emerged as the direction for communication network development. These networks face various challenges such as extensive coverage, diverse communication node types, low-quality communication links, and simultaneous operation of multiple network protocols. However, the rapid development and widespread application of artificial intelligence and machine learning technologies in recent years have offered new perspectives and solutions for the communication architecture and routing algorithm research within space-air-ground integrated networks. In these networks, not all nodes can typically communicate directly with satellites; instead, a specific set of specialized communication nodes facilitates data communication between aerial and satellite networks due to their superior communication capabilities. Consequently, in contrast to traditional communication architectures, space-air-ground integrated networks, particularly in the terrestrial layer, often need to address challenges related to the diversity of communication node types and low-quality communication links. A well-designed routing approach becomes crucial in addressing these issues. Therefore, this paper proposes an AODV routing network protocol based on an improved ant colony algorithm (AC-AODV), specifically designed for the terrestrial layer within the space-air-ground integrated networks. By integrating information such as the type, energy, and location of communication nodes, this protocol aims to facilitate network communication. The objective is to guide information flow through nodes that are more suitable for communication, either by relaying communication or by connecting with satellites through specialized nodes. This approach alleviates the burden on ordinary nodes within the terrestrial communication network, thereby enhancing the overall network performance. In this protocol, specialized nodes hold a higher forwarding priority than regular nodes. When a source node needs to transmit data, it enters the route discovery phase, utilizing its own type, location, and energy information as heuristic data to calculate forwarding probabilities. Subsequently, it broadcasts route request (RREQ) messages to find the path. Upon receiving the RREQ message, the destination node sends an RREP message for updating information elements and selects the optimal path based on these information elements. Compared to AODV, AC-AODV shows significant improvements in performance metrics such as transmission latency, throughput, energy conversion rate, and packet loss rate.

Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks

Novel architectures incorporating transport networks and artificial intelligence (AI) are currently being developed for beyond 5G and 6G technologies. Given that the interfacing mobile and transport network nodes deliver high transactional packet volume in downlink and uplink streams, 6G networks envision adopting diverse transport networks, including non-terrestrial types of transport networks such as the satellite network, High-Altitude Platform Systems (HAPS), and DOCSIS cable TV. Hence, there is a need to match the traffic to the transport network. This paper focuses on such a matching problem and defines a method that leverages machine learning and mixed-integer linear programming. Consequently, the proposed scheme in this paper is to develop a traffic steering capability based on types of transport networks, namely, optical, satellite, and DOCSIS cable. Novel findings demonstrate a more than 90% accuracy of steered traffic to respective types of transport networks for dedicated transport network resources.

CYBERSECURITY ANALYTICS IN PROTECTING SATELLITE TELECOMMUNICATIONS NETWORKS: A CONCEPTUAL DEVELOPMENT OF CURRENT TRENDS, CHALLENGES, AND STRATEGIC RESPONSES

Cybersecurity is a critical concern in satellite telecommunications networks, given their vulnerability to cyber threats. This abstract presents a conceptual development of current trends, challenges, and strategic responses in using cybersecurity analytics to protect these networks. The paper discusses the increasing reliance on satellite telecommunications, making them attractive targets for cyber attacks. It explores the role of cybersecurity analytics in detecting and mitigating these threats, highlighting the importance of proactive monitoring and threat intelligence. Challenges in cybersecurity analytics for satellite networks are identified, including the complexity of satellite systems, the limited visibility into network traffic, and the evolving nature of cyber threats. The paper discusses strategic responses to these challenges, such as the use of advanced analytics techniques, machine learning, and artificial intelligence to enhance threat detection and response capabilities. Key trends in cybersecurity analytics for satellite networks are examined, including the growing adoption of cloud-based security solutions, the rise of insider threats, and the need for collaboration between satellite operators and cybersecurity experts. The paper also discusses the importance of regulatory compliance and the role of industry standards in ensuring the security of satellite networks. In conclusion, the paper emphasizes the importance of cybersecurity analytics in protecting satellite telecommunications networks and recommends a proactive approach to cybersecurity that includes continuous monitoring, threat intelligence sharing, and collaboration with cybersecurity experts.
 Keywords: Cybersecurity, Analytics, Satellite, Telecommunications, Conceptual Development.

MARKET EXPANSION AND COMPETITIVE POSITIONING IN SATELLITE TELECOMMUNICATIONS: A REVIEW OF ANALYTICS-DRIVEN STRATEGIES WITHIN THE GLOBAL LANDSCAPE

Market expansion and competitive positioning are paramount in the dynamic landscape of satellite telecommunications. This review explores the role of analytics-driven strategies in achieving these objectives on a global scale. Satellite telecommunications providers face intense competition, technological advancements, and evolving customer expectations, necessitating innovative approaches to gain a competitive edge. Analytics plays a crucial role in understanding market trends, consumer behavior, and competitor strategies. By leveraging advanced analytics techniques, companies can uncover actionable insights to inform strategic decision-making. One key area where analytics is instrumental is in identifying untapped market opportunities. Through data analysis, companies can pinpoint underserved regions or niche markets, enabling targeted expansion efforts. Moreover, analytics enables companies to optimize their service offerings and pricing strategies. By analyzing customer preferences, usage patterns, and market dynamics, companies can tailor their offerings to meet specific customer needs. This personalized approach not only enhances customer satisfaction but also improves profitability. Analytics also plays a vital role in enhancing operational efficiency. By analyzing data from satellite networks, companies can identify potential bottlenecks, optimize resource allocation, and improve overall network performance. This not only reduces operational costs but also enhances service reliability and quality. Furthermore, analytics-driven strategies are instrumental in competitive positioning. By analyzing competitor performance, market trends, and customer feedback, companies can identify areas where they can differentiate themselves. This could include offering innovative services, improving customer support, or enhancing network coverage. In conclusion, analytics-driven strategies are crucial for satellite telecommunications companies looking to expand their market presence and enhance their competitive positioning. By leveraging data analytics, companies can uncover valuable insights that inform strategic decision-making, optimize operations, and drive innovation. In an increasingly competitive landscape, the ability to harness the power of analytics is key to long-term success in the satellite telecommunications industry.
 Keywords: Market Expansion, Competitive, Positioning, Satellite, Communications.

Cloud-edge collaborative computing offloading and resource allocation optimization in LEO satellite network

Satellite edge computing can provide communication and computing services to areas lacking ground network coverage, enhancing the computational capabilities of terrestrial users. However, due to the relatively limited computational resources available on satellites, cloud-edge collaborative computing in Low Earth Orbit (LEO) satellite communication scenarios emerges as a more optimal solution. In this paper, a cloud-edge collaborative offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) algorithm is proposed to solve the offloading decision and resource allocation problems. The optimization problem is modeled as Markov decision process, and the comprehensive delay and energy consumption cost of computing tasks completed by ground users are optimized. Simulation results validate that the proposed algorithm has good convergence properties and lower comprehensive cost than processing all tasks locally and offloading all tasks to LEO satellites.

Analyzing the robustness of LEO satellite networks based on two different attacks and load distribution methods.

Low earth orbit (LEO) satellite constellations have emerged as a promising architecture integrated with ground networks, which can offer high-speed Internet services to global users. However, the security challenges faced by satellite networks are increasing, with the potential for a few satellite failures to trigger cascading failures and network outages. Therefore, enhancing the robustness of the network in the face of cascading failures is of utmost importance. This paper aims to explore the robustness of LEO satellite networks when encountering cascading failures and then proposes a modeling method based on virtual nodes and load capacity. In addition, considering that the ground station layout and the number of connected satellites together determine the structure of the final LEO satellite network, we here propose an improved ground station establishment method that is more suitable for the current network model. Finally, the robustness of the LEO satellite networks is deeply studied under two different attacks and cost constraints. Simulations of LEO satellite networks with different topologies show that the maximum load attacks have a destructive impact on the network, which can be mitigated by adjusting the topology and parameters to ensure normal network operation. The current model and related results provide practical insights into the protection of LEO satellite networks, which can mitigate cascading risks and enhance the robustness of LEO systems.

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

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

A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks

Mega-constellation network traffic forecasting provides key information for routing and resource allocation, which is of great significance to the performance of satellite networks. However, due to the self-similarity and long-range dependence (LRD) of mega-constellation network traffic, traditional linear/non-linear forecasting models cannot achieve sufficient forecasting accuracy. In order to resolve this problem, a mega-constellation network traffic forecasting model based on EMD (empirical mode decomposition)-ARIMA (autoregressive integrated moving average) and IGWO (improved grey wolf optimizer) optimized BPNN (back-propagation neural network) is proposed in this paper, which makes comprehensive utilization of linear model ARIMA, non-linear model BPNN and optimization algorithm IGWO. With the enhancement of the global optimization capability of a BPNN, the proposed hybrid model can fully realize the potential of mining linear and non-linear laws of mega-constellation network traffic, hence improving the forecasting accuracy. This paper utilizes an ON/OFF model to generate historical self-similar traffic to forecast. RMSE (root mean square error), MAE (mean absolute error), R-square and MAPE (mean absolute percentage error) are adopted as evaluation indexes for the forecasting effect. Comprehensive experimental results show that the proposed method outperforms traditional constellation network traffic forecasting schemes, with several improvements in forecasting accuracy and efficiency.

A Deep-Learning Approach to a Volumetric Radio Environment Map Construction for UAV-Assisted Networks

Providing global coverage for ubiquitous users is a key requirement of the fifth generation (5G) and beyond wireless technologies. This can be achieved by integrating airborne networks, such as unmanned aerial vehicles (UAVs) and satellite networks, with terrestrial networks. However, the deployment of airborne networks in a three-dimensional (3D) or volumetric space requires a new understanding of the propagation channel and its losses in both the areal and altitude dimensions. Despite significant research on radio environment map (REM) construction, much of it has been limited to two-dimensional (2D) contexts. This neglects the altitude-related characteristics of electromagnetic wave propagation and confines REMs to 2D formats, which limits the comprehensive and continuous visualization of propagation environment variation in spatial dimensions. This paper proposes a volumetric REM (VREM) construction approach to compute 3D propagation losses. The proposed approach addresses the limitations of existing approaches by learning the spatial correlation of wireless propagation channel characteristics and visualizing REM in areal and height/altitude dimensions using deep learning models. Specifically, the approach uses two deep learning-based models: volume-to-volume (Vol2Vol) VREM with 3D-generative adversarial networks and sliced VREM with altitude-aware spider-UNets. In both cases, knowledge of the propagation environment and transmitter locations in 3D space is used to capture the spatial and altitude dependency of the propagation channel’s characteristics. We developed the Addis dataset, a large REM dataset comprising 54,000 samples collected from the urban part of Addis Ababa, Ethiopia, to train the proposed models. Each sample of data comprises a 512-meter by 512-meter areal resolution with different 3D obstacles (buildings and terrain), 15 simulated propagation loss maps at every 3-meter altitude resolution, and 80 different 3D transmitter locations. The results of the training and testing of the proposed models reveal that the constructed VREMs are statistically comparable. In particular, the Vol2Vol approach has a minimum L1 loss of 0.01, which further decreases to 0.0084 as the line-of-sight (LoS) probability increases to 0.95.

Transformer-based anomaly detection in P-LEO constellations: A dynamic graph approach

Successful management of large space systems, such as the most recent Proliferated Low Earth Orbit (P-LEO) constellations, demands an increased level of autonomy and irregular behavior detection capability. Existing techniques for satellite network management rely on monitoring satellites individually, potentially failing to capture events shared across multiple units. In this work, we propose a pipeline for identifying anomalous connections in proliferated satellite networks. Our pipeline relies on representing a satellite constellation as a dynamic graph. Dynamic graphs can be conveniently exploited to represent P-LEO networks, as they allow to capture meaningful structural and temporal correlations. Such spatial and temporal information are first projected into an embedding space; next, this encoded representation is processed by a transformer-encoder network for the anomaly detection task. We conduct extensive analyses of the main problem parameters, including the temporal horizon, the structure of the graph, and the size of the constellation. To assess the general performance of the method, we perform a Monte Carlo analysis on 960 ground node pair connections, providing empirical evidence of the algorithm’s generalization capability. The obtained results encourage the extension of the method to more complex, realistic modeling and kindred applications such as the on-edge detection problem.

A Local Pre-Rerouting Algorithm to Combat Sun Outage for Inter-Satellite Links in Low Earth Orbit Satellite Networks

Satellite networks show the development trend in global coverage, flexible access, and reliable transmission. They are the key to building a wide coverage, massive connection, three-dimensional, all-round, all-weather, space-, air- and ground-integrated information network. Due to its high frequency and large bandwidth, the laser inter-satellite link (ISL) is easily interfered with by sunlight. When sun outage occurs, the laser inter-satellite link will be interrupted for a long time. Most of the existing satellite link failure-routing algorithms are distributed and have poor load balancing capabilities. The centralized algorithm calculates paths independently for different services, and the effect is not ideal. In this paper, a local pre-rerouting algorithm (LPRA) is proposed. The center node collects the link information from the limited area before sun outage and the service information in the link that is about to be interrupted, and reroutes these services before sun outage occurs. After the occurrence of sun outage, services that originally needed to go through the failed link are forwarded according to the path calculated in advance, thereby avoiding link congestion and reducing the packet loss rate. Simulation results show that the proposed algorithm performs better than existing algorithms in terms of packet loss ratio, total delay and load balancing capabilities.

Democratizing LEO Satellite Network Measurement

Low Earth Orbit (LEO) satellite networks are quickly gaining traction with promises of impressively low latency, high bandwidth, and global reach. However, the research community knows relatively little about their operation and performance in practice. The obscurity is largely due to the high barrier of entry for measuring LEO networks, which requires deploying specialized hardware or recruiting large numbers of satellite Internet customers. In this paper, we introduce HitchHiking, a methodology that democratizes global visibility into LEO satellite networks. HitchHiking builds on the observation that Internet-exposed services that use LEO Internet can reveal satellite network architecture and performance, bypassing the need for specialized hardware. We evaluate HitchHiking against ground truth measurements and prior methods, showing that it provides more coverage and accuracy. With HitchHiking, we complete the largest study to date of Starlink network latency, measuring over 2,400 users across 27 countries. We uncover unexpected patterns in latency that surface how LEO routing is more complex than previously understood. Finally, we conclude with recommendations for future research on LEO networks.

Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority

The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each workflow under the premise of considering latency and energy consumption. Therefore, in this paper, we propose a multi-DAG satellite network task scheduling algorithm based on cache-composite priority under the Software-Defined Networking satellite network architecture. The basic idea of this algorithm lies in the DAG selection phase, where not only are task priorities computed but also the concept of fair scheduling is introduced, so as to prevent the excessively delayed scheduling of low-priority DAG tasks. In addition, the concept of public subtasks is introduced to reduce the system overhead caused by repetitive tasks. The experimental results show that the hybrid scheduling strategy proposed in this paper can meet the demand of DAG scheduling and improve the degree of task completion while effectively reducing the task latency and energy consumption.

Connection-density-aware satellite-ground federated learning via asynchronous dynamic aggregation

With the development of space technology, the use of satellites for Earth observation is becoming more and more common. Among them, low-earth orbit (LEO) satellites have received a lot of attention because of their short return period and low cost. However, traditional satellite systems are unable to process data in orbit due to technical limitations and can only transmit massive amounts of data with useless information back to ground stations and then perform calculations, putting a tremendous strain on downstream bandwidth. Additionally, there are data walls between satellite affiliates, which makes it difficult to share and use data. To address the lack of data processing capabilities and data barriers in conventional satellite systems, as well as to effectively use satellite resources and enhance worldwide collaboration, we proposed a connection density-aware satellite-ground federated learning (FL) framework. According to this structure, satellites serve as workers in federated learning, analyzing data and transmitting the parameters to ground stations for aggregation. The ground station senses the satellite’s connection density during the aggregation process and implements a dynamic aggregation approach for the downstream parameters. Experiments based on real image datasets and on a simulated satellite network validate the effectiveness of the method in terms of execution efficiency and accuracy.