Low Earth Orbit Satellite Networks Research Articles

An Adaptive Dynamic Channel Allocation Algorithm Based on a Temporal–Spatial Correlation Analysis for LEO Satellite Networks

Low Earth orbit (LEO) satellites that can be used as computing nodes are an important part of future communication networks. However, growing user demands, scarce channel resources and unstable satellite–ground links result in the challenge to design an efficient channel allocation algorithm for the LEO satellite network. Edge computing (EC) provides sufficient computing power for LEO satellite networks and makes the application of reinforcement learning possible. In this paper, an adaptive dynamic channel allocation algorithm based on a temporal–spatial correlation analysis for LEO satellite networks is proposed. First, according to the user mobility model, the temporal–spatial correlation of handoff calls is analyzed. Second, the dynamic channel allocation process in the LEO satellite network is formally described as a Markov decision process. Third, according to the temporal–spatial correlation, a policy for different call events is designed and online reinforcement learning is used to solve the channel allocation problem. Finally, the simulation results under different traffic distributions and different traffic intensities show that the proposed algorithm can greatly reduce the rejection probability of the handoff call and then improve the total performance of the LEO satellite network.

A Cooperative Routing Scheme Using Inter-Satellite Links to Assist Data Downloading for LEO Satellite Networks.

Low earth orbit (LEO) satellite networks can provide Internet service to users in areas where cellular networks are difficult to deploy. One critical function of satellites is to transfer data from satellite networks to ground core network through earth stations (ESs). The Ka-band multiple-input multiple-output (MIMO) can be used to establish feeder links with larger bandwidth between satellites and ESs. However, propagation at the Ka band is subjected to rain attenuation and various atmospheric fading mechanisms, which severely reduce the maximum link capacity. As a result, the insufficient capacity of feed link becomes the throughput bottleneck of satellite networks. In order to increase network throughput, it is important to fully use feeder link resources. In this paper, we propose a cooperation scheme to route packets to ESs through a well-resourced feeder link, such that the bandwidth of the feeder links can be fully utilized and the throughput of data downloading at the ESs is maximized. Firstly, we model the satellite network system and the feeder link based on MIMO technology. Then, a Maximum-Flow-Minimum-Cost (MCMF) routing algorithm consisting of two Linear Programs (LPs) is presented to compute maximum-flow routings for data download. Eventually, a variety of simulations are conducted to assess the proposed scheme, which shows that the cooperative routing scheme outperforms the existing SiRRS method in terms of throughput and delay.

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.

Evaluating the Accuracy of Stochastic Geometry Based Models for LEO Satellite Networks Analysis

This letter investigates the accuracy of recently proposed stochastic geometry-based modeling of low earth orbit (LEO) satellite networks. In particular, we use the Wasserstein Distance-inspired method to analyze the distances between different models, including Fibonacci lattice and orbit models. We propose an algorithm to calculate the distance between the generated point sets. Next, we test the algorithm’s performance and analyze the distance between the stochastic geometry model and other more widely acceptable models using numerical results.

An Efficient Location-Based Forwarding Strategy for Named Data Networking and LEO Satellite Communications

Low Earth orbit (LEO) satellite constellations are increasingly gaining attention as future global Internet providers. At the same time, named data networking (NDN) is a new data-centric architecture that has been recently proposed to replace the classic TCP/IP architecture since it is particularly well suited to the most common usage of the Internet nowadays as a content delivery network. Certainly, the use of NDN is especially convenient in highly dynamic network environments, such as those of next LEO constellations incorporating inter-satellite links (ISL). Among other native facilities, such as inbuilt security, NDN readily supports the mobility of clients, thus helping to overcome one of the main problems raised in LEO satellite networks. Moreover, thanks to a stateful forwarding plane with support for multicast transmission and inbuilt data caches, NDN is also able to provide a more efficient usage of the installed transmission capacity. In this paper, we propose a new location-based forwarding strategy for LEO satellite networks that takes advantage of the knowledge of the relative position of the satellites and the grid structure formed by the ISLs to perform the forwarding of NDN packets. So, forwarding at each node is done using only local information (node and destination locations), without the need of interchanging information between nodes, as is the case with conventional routing protocols. Using simulation, we show that the proposed forwarding strategy is a good candidate to promote the efficient and effective future use of the NDN architecture in LEO satellite networks.

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.

Joint CCI Mitigation and Power Control for MC-DS-CDMA in LEO Satellite Networks

We investigate a novel downlink multicarrier direct-sequence code division multiple access (MC-DS-CDMA) resource allocation scheme in the context of low earth orbit (LEO) satellite-ground integrated networks (SGINs). In contrast to the existing MC-DS-CDMA works which mainly focus on delay-tolerant services in terrestrial networks, we consider the heterogeneous delay traffic and take the unique characteristics of LEO satellite systems into account. Specifically, we exploit the channel information, the delay requirement, the buffer state, and the visible time of users to construct a utility function and formulate the resource allocation optimization problem in presence of co-channel interferences (CCI) in LEO satellite-ground heterogeneous systems. We transform the original nonconvex optimization problem into two convex ones and use the Lagrange dual decomposition method to derive the solution. We also design an efficient algorithm dynamically scheduling subcarriers, codes, and transmission power of MC-DS-CDMA. The simulation results confirm the superiority of our work in terms of lower average delay and higher overall throughput.

Trustworthy handover in LEO satellite mobile networks

Recently, a low earth orbit (LEO) satellite network is one of major systems to provide seamless access for terrestrial network systems. In order to provide robust access, efficient handover mechanisms are essential. However, conventional mechanisms may introduce frequent handovers due to the rapid movement of satellites. To deal with this problem, this paper proposes a learning-based auction handover under the consideration of received signal strength and service time between terrestrial users and satellites. The reason why auction-based approach is utilized is that it is generally considered as trustworthy. Our experiment results verify the proposed algorithm achieves desired performance.

FybrrLink: Efficient QoS-Aware Routing in SDN Enabled Future Satellite Networks

Providing high-speed Internet services using satellite networks have attracted researchers from both academia and industry mainly due to the characteristics of Low Earth Orbit (LEO) satellite networks such as global coverage, scalability, and lower transmission delays. Implementation of Software-Defined Networking (SDN) in Non-Terrestrial Networks (NTNs) can help to achieve the goals set for 5G and beyond networks. Since satellite networks have a specific architecture, some of the legacy protocols no longer remain useful. Therefore, to satisfy the diverse Quality of Service (QoS) requirements for a variety of applications, we propose a novel and centralized QoS-aware routing algorithm, called fybrrLink in which the global view of the network in SDN is utilized. We implement a modified Bresenham’s algorithm and Dijkstra’s algorithm to find the optimal path in a significantly reduced computation time. Also, taking advantage of the deterministic satellite constellation, we propose a flow rule transfer algorithm and a topology monitoring algorithm. Further, fybrrLink is evaluated with multiple NS3 simulations, and results confirm that our approach outperforms other state-of-the-art algorithms.

Satellite Edge Computing Architecture and Network Slice Scheduling for IoT Support

For 5G and 6G communications, satellites are drawing great attention for global coverage extension and 3-D mobility enhancement. With advancements of satellite hardware, functional satellites are expected to be applied for 6G Internet of Things (IoT) services. In particular, because IoT service has a relatively low computational burden, it is more feasible for satellite edge computing (SatEC) with limited power, making IoT supportable SatEC one of the economically feasible applications for future satellite networks. In this article, an architecture of IoT supportable SatEC is analyzed, and the corresponding network slice scheduling is proposed. First, a multiobjective optimization problem for IoT supportable SatEC is formulated with respect to latency, computational power, and transmission power attenuation. The problem is solved for the satellite offloading rate and altitude by using a heuristic algorithm in low time complexity with time-varying satellite constellation topology and various service requirements for simulations. Next, to analyze the expandability of the SatEC IoT network, a sliced SatEC IoT scheduling problem is formulated in the normalized weighted sum of latency, computational power, and transmission power attenuation. Scheduling rules are proposed to prioritize various applications with the results of the scheduling problem and with the proper SatEC offloading rates predefined in the Pareto optimality of the satellite edge multiobjective Tabu search (SE-MOTS). Finally, efficient satellite constellations are determined by comparing low-Earth orbit (LEO) and very LEO (VLEO) satellite networks, in terms of proper satellite altitudes and offloading strategies for IoT supportable SatEC. Based on simulation results, a scheduling rule for sliced satellite network and a proper offloading strategy of different slices are proposed, and an appropriate altitude of the satellite network for sliced SatEC is discussed.

A regional interest-aware caching placement scheme for reducing latency in the LEO satellite networks

A regional interest-aware caching placement scheme for reducing latency in the LEO satellite networks

Binary Symmetric Polynomial-Based Protected Fair Secret Sharing and Secure Communication over Satellite Networks

The rapid establishment of the low Earth orbit (LEO) satellite network in orbit has promoted the development of satellite communication technology. However, with the reduction of access conditions of satellite networks, the problems of data protection and secure communication have attracted extensive attention. A secret sharing scheme is a cryptographic technology that can disperse risks and tolerate intrusion by dividing and storing secrets. Using secret sharing technology in satellite communication can realize information security and data confidentiality. However, if there are cheaters among the participants, existing secret sharing schemes cannot prevent cheaters from sharing secrets exclusively, even if they can detect attacks. For this reason, this paper proposes a satellite based on binary symmetric polynomials protected fair secret sharing and secure communication scheme. In satellite secret refactoring, this scheme can produce a shared session key between two participants, no other key agreement processes, and reduce the scheme in the shared secret and the actual communication satellite application complexity. Users use the session key to encrypt communication to improve security and resist external attacks. The safety and fairness of the scheme are proved against the four attack models. Compared with the existing schemes, the scheme has a lower cost of deception identification on the premise of satisfying security and fairness. This scheme does not require any cryptographic assumptions and is unconditionally secure.

Pheromone Incentivized Intelligent Multipath Traffic Scheduling Approach for LEO Satellite Networks

Low Earth Orbit (LEO) satellite networking has been an indispensable and promising concept for extending the Internet coverage of future space-air-ground integrated networks to oceanic and remote airspace. However, the topology dynamics of the LEO Satellite Network (LEO-SN) for network state perception (Li <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019) and the intermittent nature of the Inter-Satellite Links (ISLs) for multipath routing discovery (Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019, Jiang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019) both induce new complicated challenges to multipath traffic scheduling for the sake of improving the utility of the LEO-SN facility (Song <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2014, Zhang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2018, and Yang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2020). Motivated by these challenges, this paper aims to develop an AI aided intelligent multipath traffic scheduling approach for bolstering autonomous and efficient communications of LEO-SN. To achieve this, we formulate the multipath traffic scheduling problem into a pheromone incentivized Markov Decision Process (MDP) by considering ant routing protocol and adapting pheromone to LEO-SN. Employing enhanced pheromone characterizing network state, we propose ant-inspired multipath routing discovery, which is capable of promptly discovering routing paths available in the dynamic topology. To improve the utility of these discovered routing paths, we employ deep deterministic policy gradient into the pheromone-incentivized MDP-based scheduling problem to derive an intelligent multipath traffic scheduling strategy. The experimental results are further presented to show the achievable performance improvement.

Dynamic User Association in Scalable Ultra-Dense LEO Satellite Networks

Ultra-dense low earth orbit (LEO) satellite networks (LEO-UDSNs) are becoming a foundational component of future 5G and beyond. LEO-UDSNs bring unprecedented high-speed mobility, broadband capacity, and ultra-density qualities, introducing a significant challenge to traditional user association (UA) methods. In this article, we first discuss the difficulties in sequential UA optimization for scalable LEO-UDSNs. A specific multi-objective UA problem was formulated for the overall service efficiency. We initially proposed a practical Iterative Subgradient Algorithm (MOISA) decoupling potential trade-offs between limited equivalent power and availability relationship, rapidly obtaining solutions in stable cases. Second, we proposed a Multi-Objective Dynamic User Association Approach (MODUAA), inheriting the MOISA with stability enhancements for sequential optimizations, ensuring the performance requirements, and alleviating the iteration redundancy in dynamic scenarios. Finally, the outperforming of the MOISA and the applicability of the MODUAA were elaborated in various comparisons.

Waypoint segment routing algorithm for LEO satellite network

Satellite communication, especially low earth orbit satellites, an important part of the space-air-ground integrated network, is a promising solution to support global coverage and reliable communication. However, the originally connected links will be off, when the satellites move into polar regions. In addition, the condition of the path broken will dramatically increase with the number of satellites increasing. Thus, it is a continually considerable challenge to design an efficient and stationary routing algorithm for intra-satellites. This paper presents a waypoint segment routing algorithm to realise persistent routes when the satellites move into polar regions. The waypoint selection problem is modelled and solved in stages. One of the source-based routing algorithms, segment routing, is applied in low earth orbit satellite network to provide stable, efficient and reliable packets forwarding. By adding the selected waypoints into the segment list of the segment routing, the next-snapshot broken links can be avoided. Based on the proposed waypoint segment routing algorithm, the problem of the path broken can be effectively solved. By comparing the packet loss, the delay and the delay jitter of the satellite simulation networks, the proposed algorithm is preferable to the popular optimised link-state routing algorithm for the satellite mega-constellation networks.

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

Recently, mega Low Earth Orbit (LEO) Satellite Network (LSN) systems have gained more and more attention due to low latency, broadband communications and global coverage for ground users. One of the primary challenges for LSN systems with inter-satellite links is the routing strategy calculation and maintenance, due to LSN constellation scale and dynamic network topology feature. In order to seek an efficient routing strategy, a Q-learning-based dynamic distributed Routing scheme for LSNs (QRLSN) is proposed in this paper. To achieve low end-to-end delay and low network traffic overhead load in LSNs, QRLSN adopts a multi-objective optimization method to find the optimal next hop for forwarding data packets. Experimental results demonstrate that the proposed scheme can effectively discover the initial routing strategy and provide long-term Quality of Service (QoS) optimization during the routing maintenance process. In addition, comparison results demonstrate that QRLSN is superior to the virtual-topology-based shortest path routing algorithm.

A Routing Optimization Method for LEO Satellite Networks with Stochastic Link Failure

In this paper, for an Low-Earth Orbit (LEO) satellite network with inter-satellite links, a routing optimization method is developed in the case of stochastic link failure. First, a discrete-time strategy is used for the satellite network to acquire several static topological graphs during a cycle. Based on the static topological graphs regarding stochastic link failure, a constraint model is established that constructs the task revenue, switching times and routing cost as indicators. Then, an improved Genetic Algorithm based on A* is proposed to optimize the topology under the constraint model. In particular, to reduce the cost of computation, a new generation strategy for the initial solution is presented which combines the roulette wheel operator and the A* algorithm. Finally, the effectiveness of the proposed method is illustrated by a group of numerical simulations for the network with stochastic link failure.

Neural network joint capacity-power control strategy based on NSGAII-BP for interference suppression in LEO satellite uplinks

Due to the long distance between satellites and the earth, uplink users need considerable energy to achieve satellite communication. Therefore, the energy efficiency is very important for user terminals. In addition, multibeam Low Earth Orbit (LEO) satellite networks may cause cochannel interference between beams because of full frequency reuse. Moreover, power competition among users will easily occur if the users begin to privately increase the transmission power. Power competition limits capacity and reduces energy efficiency. To solve these problems, this paper proposes a power control strategy balancing capacity and energy efficiency. A dual-objective optimization model is established based on the maximum transmission power constraint and the minimum signal-to-interference ratio (SIR) constraint. The model is solved by a back propagation (BP) neural network based on the fast nondominated sorting genetic algorithm (NSGAII). The neural network can adjust the optimization degree of the energy efficiency and capacity by preference factors. The simulation experiment results show that, while the user minimum transmission rate increases, the EE optimization of the NSGAII-BP neural network is better than that of the traditional weighting method.

Efficient Uplink Transmission in Ultra-Dense LEO Satellite Networks With Multiband Antennas

In this letter, we propose an efficient uplink transmission scheme over ultra-dense low earth orbit (LEO) satellite networks with multiband antennas. A two-timescale transmission resource allocation and frequency band switching optimization framework is established with the objective of maximizing the overall data rate while reducing the band switching cost. The small-timescale transmission resource allocation problem is described as a Markov decision process (MDP) considering continuous LEO satellite movements and Markovian rainfall changes, while the large-timescale satellite band switching is formulated as an integer programming problem. A hierarchical algorithm based on the deep reinforcement learning (DRL) approach and the greedy method is proposed to make efficient decisions. Extensive simulations are conducted to validate the effectiveness and advantages of our proposed scheme.

The Digital Divide in Canada and the Role of LEO Satellites in Bridging the Gap

Overcoming the digital divide in rural and remote areas has always been a big challenge for Canada with its huge geographical area. In 2016, the Canadian Radio-television and Telecommunications Commission announced broadband Internet as a basic service available for all Canadians. However, approximately one million Canadians still did not have access to broadband services as of 2020. The COVID-19 pandemic has made the situation more challenging, as social, economic, and educational activities have increasingly been transferred online. The condition is more unfavorable for Indigenous communities. A key challenge in deploying rural and remote broadband Internet is to plan and implement high-capacity backbones, which are now available only in denser urban areas. For any Internet provider, it is almost impossible to make a viable business proposal in these areas. For example, the vast land of the Northwest Territories', Yukon's, and Nunavut's diverse geographical features present obstacles for broadband infrastructure. In this article, we investigate the digital divide in Canada with a focus on rural and remote areas. In so doing, we highlight two potential solutions using low Earth orbit (LEO) constellations to deliver broadband Internet in rural and remote areas to address the access inequality and the digital divide. The first solution involves integrating LEO constellations as a backbone for the existing 4G/5G telecommunications network. This solution uses satellites in a LEO constellation to provide a backhaul network connecting the 4G/5G access network to its core network. The 3rd Generation Partnership Project already specifies how to integrate LEO satellite networks into the 4G/5G network, and the Canadian satellite operator Telesat has already showcased this solution with one terrestrial operator, TIM Brasil, in their 4G network. In this way, users can seamlessly access broadband Internet via their mobile terminals. The second solution is based on the direct use of LEO constellations, such as Starlink, which are now operating in Canada, to deliver broadband Internet. As LEO satellites fly lower, their round-trip latency is lower, and the user terminals can receive Internet signals as long as they are pointing at the sky. An in-depth discussion of both solutions is presented in this work.