Multi-network-region traffic cooperative scheduling in large-scale LEO satellite networks

A low-Earth-orbit(LEO)satellite network can provide full-coverage access services worldwide and is an essential candidate for future 6G networking.However,the large variability of the geographic distribution of the Earth’s population leads to an uneven service volume distribution of access service.Moreover,the limitations on the resources of satellites are far from being able to serve the traffic in hotspot areas.To enhance the forwarding capability of satellite networks,we first assess how hotspot areas under different load cases and spatial scales significantly affect the network throughput of an LEO satellite network overall.Then,we propose a multi-region cooperative traffic scheduling algorithm.The algorithm migrates low-grade traffic from hotspot areas to coldspot areas for forwarding,significantly increasing the overall throughput of the satellite network while sacrificing some latency of end-to-end forwarding.This algorithm can utilize all the global satellite resources and improve the utilization of network resources.We model the cooperative multi-region scheduling of large-scale LEO satellites.Based on the model,we build a system testbed using OMNET++to compare the proposed method with existing techniques.The simulations show that our proposed method can reduce the packet loss probability by 30%and improve the resource utilization ratio by 3.69%.

For LEO Satellite Networks: Intelligent Interference Sensing and Signal Reconstruction Based on Blind Separation Technology

In LEO satellite communication networks,the number of satellites has increased sharply, the relative velocity of satellites is very fast, then electronic signal aliasing occurs from time to time. Those aliasing signals make the receiving ability of the signal receiver worse, the signal processing ability weaker,and the anti-interference ability of the communication system lower. Aiming at the above problems, to save communication resources and improve communication efficiency, and considering the irregularity of interference signals, the underdetermined blind separation technology can effectively deal with the problem of interference sensing and signal reconstruction in this scenario. In order to improve the stability of source signal separation and the security of information transmission, a greedy optimization algorithm can be executed. At the same time, to improve network information transmission efficiency and prevent algorithms from getting trapped in local optima, delete low-energy points during each iteration process. Ultimately, simulation experiments validate that the algorithm presented in this paper enhances both the transmission efficiency of the network transmission system and the security of the communication system, achieving the process of interference sensing and signal reconstruction in the LEO satellite communication system.

Delay-optimal multi-satellite collaborative computation offloading supported by OISL in LEO satellite network

By deploying the ubiquitous and reliable coverage of low Earth orbit(LEO)satellite networks using optical inter satel-lite link(OISL),computation offloading services can be provided for any users without proximal servers,while the resource limita-tion of both computation and storage on satellites is the impor-tant factor affecting the maximum task completion time.In this paper,we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs,such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood.To satisfy the delay requirement of delay-sensi-tive task,we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline,and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites.Simulation results demonstrate the effective-ness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.

Jamming-Aided Secure Communication in Ultra-Dense LEO Integrated Satellite-Terrestrial Networks

The ultra-dense low earth orbit(LEO)integrated satellite-terrestrial networks(UDLEO-ISTN)can bring lots of benefits in terms of wide coverage,high capacity,and strong robustness.Meanwhile,the broadcasting and open natures of satellite links also reveal many challenges for transmission security protection,especially for eavesdropping defence.How to efficiently take advantage of the LEO satellite’s density and ensure the secure communication by leveraging physical layer security with the cooperation of jammers deserves further investigation.To our knowledge,using satellites as jammers in UDLEO-ISTN is still a new problem since existing works mainly focused on this issue only from the aspect of terrestrial networks.To this end,we study in this paper the cooperative secrecy communication problem in UDLEOISTN by utilizing several satellites to send jamming signal to the eavesdroppers.An iterative scheme is proposed as our solution to maximize the system secrecy energy efficiency(SEE)via jointly optimizing transmit power allocation and user association.Extensive experiment results verify that our designed optimization scheme can significantly enhance the system SEE and achieve the optimal power allocation and user association strategies.

Core-based Shared Tree Multicast Routing Algorithms for LEO Satellite IP Networks

A new core-based shared tree algorithm, viz core-cluster combination-based shared tree （CCST） algorithm and the weighted version （i.e. w-CCST algorithm） are proposed in order to resolve the channel resources waste problem in typical source-based multicast routing algorithms in low earth orbit （LEO） satellite IP networks. The CCST algorithm includes the dynamic approximate center （DAC） core selection method and the core-cluster combination multicast route construction scheme. Without complicated onboard computation, the DAC method is uniquely developed for highly dynamic networks of periodical and regular movement. The core-cluster combination method takes core node as the initial core-cluster, and expands it stepwise to construct an entire multicast tree at the lowest tree cost by a shortest path scheme between the newly-generated core-cluster and surplus group members, which results in great bandwidth utilization. Moreover, the w-CCST algorithm is able to strike a balance between performance of tree cost and that of end-to-end propagation delay by adjusting the weighted factor to meet strict end-to-end delay requirements of some real-time multicast services at the expense of a slight increase in tree cost. Finally, performance comparison is conducted between the proposed algorithms and typical algorithms in LEO satellite IP networks. Simulation results show that the CCST algorithm significantly decreases the average tree cost against to the others, and also the average end-to-end propagation delay ofw-CCST algorithm is lower than that of the CCST algorithm.

Novel optimized routing algorithm for LEO satellite IP networks

A lot of routing algorithms have been proposed for low earth orbit（LEO） satellite IP networks in recent years,but most of them cannot achieve global optimization.The dynamic characters of LEO satellite networks are reflected in two aspects：topology and traffic change.The algorithms mentioned above are ＂hard routing＂ which only realize local optimization.A distributed soft routing algorithm combined with multi-agent system（MASSR） is proposed.In MASSR,mobile agents are used to gather routing information actively,and blackboard is introduced to achieve direct information exchange between agents.MASSR provides traffic adaptive routing and tracks the change of LEO satellite network topology.The performance of ant colony optimization（ACO） and MASSR are compared in Iridium constellation,and MASSR presents better end-to-end delay as well as enhanced robustness.

High-Risk LEO Satellite Network Path Detection Based on Spatial and Temporal Delay Anomaly Analysis

The gradual deployment of Low-Earth Orbit(LEO)mega constellations with inter-satellite links(ISLs)promises ubiquitous,low-latency,and high-throughput satellite network services.However,networked LEO satellites with ISLs are also at risk of routing attacks such as hijacking.Existing defenses against route hijacking in terrestrial networks can hardly work for the LEO satellite network due to its high spatiotemporal dynamics.To deal with it,we propose RPD,a high-risk routing path detection method for LEO mega-constellation networks.RPD detects abnormal high-risk LEO network paths by checking the consistency between the path delay and the geographical distance.This is efficiently achieved by combining in-band measurements and out-of-band statistical processing to detect the anomaly of the clustering feature in the reference delay matrix.RPD avoids the recalculation of the header cryptographic marks when the handover occurs,thus greatly reducing the cost and improving the performance of highrisk path detection.Experiments showed that the proposed RPD mechanism achieves an average detection accuracy of 91.64%under normal network conditions,and maintain about 89%even when congestion occurs in multiple areas of the network and measurement noise is considered.In addition,RPD does not require any cryptographic operation on the intermediate node,only minimal communication cost with excellent scalability and deployability.

A Discrete-Time Traffic and Topology Adaptive Routing Algorithm for LEO Satellite Networks

“Minimizing path delay” is one of the challenges in low Earth orbit (LEO) satellite network routing algo-rithms. Many authors focus on propagation delays with the distance vector but ignore the status information and processing delays of inter-satellite links. For this purpose, a new discrete-time traffic and topology adap-tive routing (DT-TTAR) algorithm is proposed in this paper. This routing algorithm incorporates both inher-ent dynamics of network topology and variations of traffic load in inter-satellite links. The next hop decision is made by the adaptive link cost metric, depending on arrival rates, time slots and locations of source-destination pairs. Through comprehensive analysis, we derive computation formulas of the main per-formance indexes. Meanwhile, the performances are evaluated through a set of simulations, and compared with other static and adaptive routing mechanisms as a reference. The results show that the proposed DT-TTAR algorithm has better performance of end-to-end delay than other algorithms, especially in high traffic areas.

A multi-path routing algorithm of LEO satellite networks based on an improved ant colony system

Geography rectangle is used to reduce signaling overhead of the LEO satellite networks.Moreover,a multi-path routing algorithm based on an improved ant colony system(MPRA-AC) is proposed.Matrix indicating the importance of the link between satellites is introduced into MPRA-AC in order to find the optimal path more quickly.Simulation results show that MPRA-AC reduces the number of iterations to achieve a satisfactory solution.At the same time,the packet delivery ratio of LEO satellite networks when running MPRA-AC and DSR-LSN(dynamic source routing algorithm for LEO satellite networks) is compared.The packet delivery ratio is about 7.9%lower when running DSR-LSN.Moreover,because of the mechanism of active load balancing of MPRA-AC,simulation results show that MPRA-AC outperforms DSR-LSN in link utilization when data packets are transmitted in the networks.

Handover Traffic Model and Channel Occupancy Time in LEO Satellite Networks

This paper presents a method of determining handover traffic and mean channel occu-pancy time of a traffic model for the LEO(Low Earth Orbit)satellite networks.The mainideas are that the handover traffic is mainly due to the movement of the satellite and that thevelocity of the mobile terminals and earth rotation are ignored.The performance level can becalculated according to different handover queuing models.

A Model for the Handover Traffic and Channel Occupancy Timein LEO Satellite Networks

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A Handover Strategy in the LEO Satellite-Based Constellation Networks with ISLs

A new handover strategy named minimal-hops handover(MHH) strategy for the lowearth orbit(LEO) satellite constellations networks equipped with inter-satellite links(ISLs) is proposed.MHH strategy,which is based on the hops of the end-to-end connection paths and makes good use of theregularity of the constellation network topology,can appropriately combine the handover procedure withrouting and efficiently solve the inter-satellite handover issue.Moreover,MHH strategy can providequality of services(QoS) guarantees to some extent.The system performances of the MHH strategy,suchas time propagation delay and handover frequency,are evaluated and compared with that of otherprevious strategies.The simulation results show that MHH strategy performs better than other previoushandover strategies.

Research on handover algorithm to reduce the blocking probability in LEO satellite network

Based on the characteristics of guaranteed handover （GH） algorithm, the finite capacity in one system makes the blocking probability （PB） of GH algorithm increase rapidly in the case of high traffic losd. So, when large amounts of multimedia services are transmitted via a single low earth orbit （LEO） satellite system, the PB of it is much higher. In order to solve the problem, a novel handover scheme defined by multi-tier optimal layer selection is proposed. The scheme sufficiently takes into account the characteristics of double-tier satellite network, which is constituted by LEO satellites combined with medium earth orbit （MEO） satellites, and the multimedia transmitted by such network, so it can augment this systematic capacity and effectively reduces the traffic loed in the LEO which performs GH algorithm. The detailed processes are also presented. The simulation and numerical results show that the approach integrated with GH algorithm achieves a significant improvement in the PB and practicality, as compared to the single LEO layer network.

Reliability Evaluation of All-User Terminals in LEO Satellite Communication Network Based on Modular Reduction

LEO satellite communication network has a large number of satellites distributed in low orbits,which leads to multiple coverage of many areas on the ground.It is hard work to describe and evaluate the reliability of LEO satellite communication network.To solve this problem,the reliability of all-user terminals in LEO satellite communication network is defined,and the corresponding reliability evaluation method is proposed in the paper.Due to the large scale of the interstellar network,a modular reduction algorithm using the modular network instead of the original network for state decomposition is proposed in this paper.Case study shows that the calculation time of the proposed method is equivalent to 6.28%of the original state space decomposition algorithm.On this basis,the reliability of LEO satellite communication network is further analyzed.It is found that the reliability of LEO satellite network was more sensitive to the reliability of Inter-Satellite link and the satisfaction of global coverage in the early stage,and it is more sensitive to the reliability of the satellite in the later stage.The satellite-ground link has a relatively constant impact on of LEO satellite network.

A DISTRIBUTED QOS ROUTING BASED ON ANT ALGORITHM FOR LEO SATELLITE NETWORK

Low Earth Orbit (LEO) satellites provide short round-trip delays and are becoming in- creasingly important. One of the challenges in LEO satellite networks is the development of specialized and efficient routing algorithms. To satisfy the QoS requirements of multimedia applications, satellite routing protocols should consider handovers and minimize their effect on the active connections. A distributed QoS routing scheme based on heuristic ant algorithm is proposed for satisfying delay bound and avoiding link congestion. Simulation results show that the call blocking probabilities of this al- gorithm are less than that of Shortest Path First (SPF) with different delay bound.

Design and verification of a satellite-terrestrial integrated IP network model for the Global Energy Interconnection

In order to meet the pressing demand for wide-area communication required by the Global Energy Interconnection(GEI),accelerating the construction of satellite-terrestrial Integra怕d networks that can achieve network extension and seamless global coverage has become the focus of power communication tech no logy development.In this study,we propose a satellite-terrestrial integrated network model that can support interconnection and interoperation on the IP layer between the satellite system and the怕rrestrial segment of the existing power communication system.First,the composition and function of the satellite-terrestrial collaborative network are explained.Then,the IP-based protocol stack is described,and a typical applicati on experime nt is con ducted to illustrate the particular process of this protocol stack.Fin ally,a use case of IP interconn ection that depends on GEO satellite communication is detailed.The experime ntal study has showed that the satellite-terrestrial collaborative network can efficiently support various IP applications for the GEI.

Distributed Deep Learning for Cooperative Computation Offloading in Low Earth Orbit Satellite Networks

Low earth orbit(LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the“ubiquitous connection” of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading(DDLCCO) algorithm, where multiple parallel deep neural networks(DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.