Low Earth Orbit Satellite Networks Research Articles

An online integrated satellite–terrestrial IoT task offloading and service deployment strategy

As a core component of 6G networking, low earth orbit (LEO) satellite networks are considered a promising solution for providing network services in remote areas and have received considerable attention in recent years. Meanwhile, with the rapid development of IoT terminals and applications, an increasing emphasis has been placed on task offloading for real-time tasks. However, most existing works on task offloading for integrated satellite–terrestrial IoT (IST-IoT) applications are based on the assumption that the global task and network information is known, an assumption that is obviously not met in online real-time task offloading. In addition, current studies rarely consider the tradeoff among the task offloading volume, satellite endurance and wholesale cost of terrestrial resources, which are precisely the holistic issues that satellite network operators must confront. To address such issues, this study considers task offloading, service deployment and resource allocation in highly dynamic online task offloading scenarios with the goal of maximizing the average task offloading volume. Moreover, virtual queues are constructed in this study to account for the impacts of long-term satellite endurance and terrestrial network resource costs on task offloading. We first decouple the original problem to obtain a single-time-slot optimization problem within the Lyapunov optimization framework, and we then propose a 2-stage optimization approach based on deep reinforcement learning (DRL) to solve the complex single-time-slot mixed integer nonlinear programming (MINLP) problem. Simulation experiments demonstrate that our proposed algorithm can yield near-optimal solutions that show superior performance compared to existing results.

Modeling Interference From Millimeter Wave and Terahertz Bands Cross-Links in Low Earth Orbit Satellite Networks for 6G and Beyond

Modeling Interference From Millimeter Wave and Terahertz Bands Cross-Links in Low Earth Orbit Satellite Networks for 6G and Beyond

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

Joint Beam Scheduling and Beamforming Design for Cooperative Positioning in Multi-Beam LEO Satellite Networks

Joint Beam Scheduling and Beamforming Design for Cooperative Positioning in Multi-Beam LEO Satellite Networks

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.

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 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.

Enabling Data Sharing Through Data Trusts in LEO Satellite Internet

Due to the advantages of low latency, small propagation loss, and global coverage, LEO satellite network becomes a cost-effective long-distance communication solution for IoT devices in remote areas. Nevertheless, the LEO satellite IoT devices deployed for specific tasks are idle most of the time, raising the obvious question for future research: how to effectively share these idle sensing resources. Fortunately, data trusts emerge as a promising strategy to leverage the idle sensing resources. However, the data trust model faces privacy and security challenges in LEO satellite IoT scenarios to achieve trusted data trading. Therefore, this paper proposes a blockchainenabled data trust model and a trusted data trading scheme for device providers, data trustees, and general users. Furthermore, exploiting the homomorphic encryption technology, we realize the fine-grained data query and device match in the premise of privacy protection for device providers and general users. Lastly, considering the openness of satellite-terrestrial communication links and the limited computing power of LEO satellite IoT devices, we design a non-interactive lightweight authentication protocol based on zero-knowledge proof. The simulation results verify the feasibility, effectiveness, and security performance of the proposed data sharing for LEO satellite IoT.

Satellite-Terrestrial Integrated 6G: An Ultra-Dense LEO Networking Management Architecture

With the rapid development of low earth orbit (LEO) satellites, the ultra-dense LEO satellite-terrestrial integrated networking has become a promising paradigm to provide wide coverage, high capacity and flexible services for the sixth generation (6G) mobile communication networks. However, the natural heterogeneity, high dynamics, and large scale of ultradense LEO satellite networks pose new challenges to network control and management. To this end, in this article, we propose a medium earth orbit (MEO), low earth orbit (LEO) and satellite Earth stations (SESs) integrated multi-layered management architecture for the ultra-dense LEO satellite network, where global controllers and local controllers are introduced to reduce the network management complexity. With the aid of this hybrid multi-layered management framework, we can efficiently implement the network status control, mobility management, resource management, and service management in the satelliteterrestrial integrated network. To obtain proper global and local controllers with high management efficiency in the hybrid multilayered network management architecture, we further propose a novel and efficient grouping and clustering method for the ultradense LEO satellite network, where each group manager MEO satellite and cluster head (CH) LEO satellites are respectively considered as the global and local controllers of each LEO satellite group. Numerical simulations are carried out to evaluate the superiority and effectiveness of the proposed hybrid multilayered ultra-dense LEO networking management architecture.

For LEO satellite networks: Intelligent interference sensing and signal reconstruction based on blind separation technology

For LEO satellite networks: Intelligent interference sensing and signal reconstruction based on blind separation technology

A Blockchain-based Privacy Protection Protocol using Smart Contracts in LEO satellite networks

A Blockchain-based Privacy Protection Protocol using Smart Contracts in LEO satellite networks

Improving Broadcast System of Integrated Satellite-Terrestrial Network-based on Enhanced Ant colony Optimization

With the use of seamless high-speed worldwide network connectivity in future, it is anticipated that the integrated satellite-terrestrial network (ISTN) will be a possible option. Due to the inadequacy of topological data and the close coupling of data and control planes, deploying optimized rules on routers is difficult. The important factors in ISTN networks are routing rules and policies, link failure, and high-bandwidth communication. Software-defined networking (SDN) is an open innovation approach that enables programmability from a central location. The controller handles the complexity of the network, whereas the infrastructure layer devices relay the packets. Thus, we investigated the broadcast by dynamically adjusting routes for fault tolerance and energy efficiency of the ISTN using distance between nodes. In addition, using dynamic source routing, this study examines the competence functions of all managing nodes in a network. The routing design is distributed among all nodes to create numerous collective paths. For LEO satellite networks, the ant colony optimization-based routing algorithm is an improved version that considers the probability of faults and low-energy consumption. The proposed simulation ensures a seamless transition in the event of failures and avoids the requirement for an additional coordination service.

Distributed Anti-Cascading Routing Scheme Based on Fuzzy Logic in LEO Satellite Networks

Distributed Anti-Cascading Routing Scheme Based on Fuzzy Logic in LEO Satellite Networks

Rate-Splitting for Joint Unicast and Multicast Transmission in LEO Satellite Networks with Non-Uniform Traffic Demand

Rate-Splitting for Joint Unicast and Multicast Transmission in LEO Satellite Networks with Non-Uniform Traffic Demand