Service Function Chain Migration in LEO Satellite Networks

With the advancements of software defined network(SDN)and network function virtualization(NFV),service function chain(SFC)placement becomes a crucial enabler for flexible resource scheduling in low earth orbit(LEO)satellite networks.While due to the scarcity of bandwidth resources and dynamic topology of LEO satellites,the static SFC placement schemes may cause performance degradation,resource waste and even service failure.In this paper,we consider migration and establish an online migration model,especially considering the dynamic topology.Given the scarcity of bandwidth resources,the model aims to maximize the total number of accepted SFCs while incurring as little bandwidth cost of SFC transmission and migration as possible.Due to its NP-hardness,we propose a heuristic minimized dynamic SFC migration(MDSM)algorithm that only triggers the migration procedure when new SFCs are rejected.Simulation results demonstrate that MDSM achieves a performance close to the upper bound with lower complexity.

MTCR-CR Routing Strategy for Connection-Oriented Routing over Satellite Networks

The high-speed movement of satellites makes it not feasible to directly apply the mature routing scheme on the ground to the satellite network.DT-DVTR in the snapshot-based connectionoriented routing strategy is one of the representative solutions,but it still has room for improvement in terms of routing stability.In this paper,we propose an improved scheme for connection-oriented routing strategy named the Minimal Topology Change Routing based on Collaborative Rules(MTCR-CR).The MTCR-CR uses continuous time static topology snapshots based on satellite status to search for intersatellite link(ISL)construction solutions that meet the minimum number of topology changes to avoid route oscillations.The simulation results in Beidou-3 show that compared with DT-DVTR,MTCR-CR reduces the number of routing changes by about 92%,the number of path changes caused by routing changes is about38%,and the rerouting time is reduced by approximately 47%.At the same time,in order to show our algorithm more comprehensively,the same experimental index test was also carried out on the Globalstar satellite constellation.

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.

An Integrated Image Task Planning in Satellite Networks:From Instruction Release and Observation Perspective

The unreasonable observation arrangements in the satellite operation control center(SOCC)may result in the observation data cannot be downloaded as scheduled.Meanwhile,if the operation instructions released by the satellite telemetry tracking center(STTC)for the on-board payloads are not injected on the specific satellites in time,the corresponding satellites cannot perform the observation operations as planned.Therefore,there is an urgent need to design an integrated instruction release,and observation task planning(I-IRO-TP)scheme by efficiently collaborating the SOCC and STTC.Motivated by this fact,we design an interaction mechanism between the SOCC and the STTC,where we first formulate the I-IRO-TP problem as a constraint satisfaction problem aiming at maximizing the number of completed tasks.Furthermore,we propose an interactive imaging task planning algorithm based on the analysis of resource distribution in the STTC during the previous planning periods to preferentially select the observation arcs that not only satisfy the requirements in the observation resource allocation phase but also facilitate the arrangement of measurement and control instruction release.We conduct extensive simulations to demonstrate the effectiveness of the proposed algorithm in terms of the number of completed tasks.

Software defined satellite networks:A survey

In recent years,satellite networks have been proposed as an essential part of next-generation mobile communication systems.Software defined networking techniques are introduced in satellite networks to handle the growing challenges induced by time-varying topology,intermittent inter-satellite link and dramatically increased satellite constellation size.This survey covers the latest progress of software defined satellite networks,including key techniques,existing solutions,challenges,opportunities,and simulation tools.To the best of our knowledge,this paper is the most comprehensive survey that covers the latest progress of software defined satellite networks.An open GitHub repository is further created where the latest papers on this topic will be tracked and updated periodically.Compared with these existing surveys,this survey contributes from three aspects:(1)an up-to-date SDN-oriented review for the latest progress of key techniques and solutions in software defined satellite networks;(2)an inspiring summary of existing challenges,new research opportunities and publicly available simulation tools for follow-up studies;(3)an effort of building a public repository to track new results.

Dynamic Routing Algorithm for Increasing Robustness in Satellite Networks

In low earth orbit（LEO） and medium earth orbit（MEO） satellite networks, the network topology changes rapidly because of the high relative speed movement of satellites. When some inter-satellite links （ISLs） fail, they can not be repaired in a short time. In order to increase the robustness for LEO/MEO satel- lite networks, an effective dynamic routing algorithm is proposed. All the routes to a certain node are found by constructing a destination oriented acyclic directed graph（DOADG） with the node as the destination. In this algorithm, multiple routes are provided, loop-free is guaranteed, and as long as the DOADG maintains, it is not necessary to reroute even if some ISLs fail. Simulation results show that comparing to the conventional routing algorithms, it is more efficient and reliable, costs less transmission overhead and converges faster.

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.

A Novel Onboard-gateway-based Mechanism to Improve TCP Performance in Aeronautical Satellite Networks

The IP-based networks on aircraft serve to support Intemet services via satellites. However, in aeronautical satellite hybrid networks, the TCP protocol performance often deteriorates due to improper decreases and slow recovery of the congestion window. This paper proposes a window size determination and notification mechanism, onboard-gateway-based mechanism （OGBM）, which is based on the onboard gateway in the networks on aircraft. A cross-layer approach is adopted by the onboard gateway to obtain the satellite link bandwidth information. And then, by the gateway, through changing the receiver＇s advertised window field in ACK packets, TCP sources are notified of the window size of each TCP source calculated on the ground of bandwidth delay product and flow numbers. The mechanism is able to avoid improper changes of TCP window and serve multiple users. Simulation results show that the mechanism with the fairness index close to l improves TCP performance in aeronautical satellite networks.

QoS-Aware Power Allocation Scheme for Relay Satellite Networks

This paper investigated a QoS-aware power allocation for relay satellite networks.For the given QoS requirements,we analyzed the signal model of relay transmission and formulated the power minimization problem which is non-convex and difficult to solve.To find the optimal solution to the considered problem,we first analyzed the optimization problem and equivalently turn it into a convex optimization problem.Then,we provided a Lagrangian dual-based method to obtain the closed-form of the power allocation and provided an iterative algorithm to the optimal solution.Moreover,we also extended the results to the cooperative transmission mode.Finally,simulation results were provided to verify the superiority of the proposed algorithm.

Energy-Efficient Traffic Offloading for RSMA-Based Hybrid Satellite Terrestrial Networks with Deep Reinforcement Learning

As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.

Average Secrecy Capacity of the Reconfigurable Intelligent Surface-Assisted Integrated Satellite Unmanned Aerial Vehicle Relay Networks

Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.

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-toend 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 multipath routing algorithm for satellite networksbased on service demand and traffic awareness

With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates,satellites have become an important part of data transmission in air-ground networks.However,due to the factors such as geographical location and people’s living habits,the differences in user’demand for multimedia data will result in unbalanced network traffic,which may lead to network congestion and affect data transmission.In addition,in traditional satellite network transmission,the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner,which is not conducive to calculating optimal routes.The service quality requirements cannot be satisfied when multiple service requests are made.Based on the above,in this paper artificial intelligence technology is applied to the satellite network,and a software-defined network is used to obtain the global network information,perceive network traffic,develop comprehensive decisions online through reinforcement learning,and update the optimal routing strategy in real time.Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability.Compared with traditional routing,the throughput is 8%higher,and the proposed method has load balancing characteristics.

Secrecy Outage Probability for Two-Way Integrated Satellite Unmanned Aerial Vehicle Relay Networks with Hardware Impairments

In this paper,we investigate the secrecy outage performance for the two-way integrated satellite unmanned aerial vehicle relay networks with hardware impairments.Particularly,the closed-form expression for the secrecy outage probability is obtained.Moreover,to get more information on the secrecy outage probability in a high signalto-noise regime,the asymptotic analysis along with the secrecy diversity order and secrecy coding gain for the secrecy outage probability are also further obtained,which presents a fast method to evaluate the impact of system parameters and hardware impairments on the considered network.Finally,Monte Carlo simulation results are provided to show the efficiency of the theoretical analysis.

UMR: A utility-maximizing routing algorithm for delay-sensitive service in LEO satellite networks

This paper develops a routing algorithm for delay-sensitive packet transmission in a low earth orbit multi-hop satellite network consists of micro-satellites. The micro-satellite low earth orbit （MS-LEO） network endures unstable link connection and frequent link congestion due to the uneven user distribution and the link capacity variations. The proposed routing algorithm, referred to as the utility maximizing routing （UMR） algorithm, improve the network utility of the MS-LEO network for carrying flows with strict end-to-end delay bound requirement. In UMR, first, a link state parameter is defined to capture the link reliability on continuing to keep the end-to-end delay into constraint; then, on the basis of this parameter, a routing metric is for- mulated and a routing scheme is designed for balancing the reliability in delay bound guarantee among paths and building a path maximizing the network utility expectation. While the UMR algo- rithm has many advantages, it may result in a higher blocking rate of new calls. This phenomenon is discussed and a weight factor is introduced into UMR to provide a flexible performance option for network operator. A set of simulations are conducted to verify the good performance of UMR, in terms of balancing the traffic distribution on inter-satellite links, reducing the flow interruption rate, and improving the network utility.

Load balancing routing for single-layered satellite networks

The varying population density leads to imbalanced utilization rate of satellites. To ensure an intelligent engineering of traffic over satellite networks, a distributed routing scheme for single-layered satellite network, load balancing routing protocol based on mobile agent （LBRP-MA） is proposed. For LBRP-MA, mobile agents explore route by migrating autonomously. Upon arriving at destination, mobile agents migrate back. On each intermediate satellite, mobile agents evaluate path cost considering satellite geographical position as well as inter-satellite link （ISL） cost, and finally take ISL congestion index into account to update routing tables. Through simulations on the Courier-like constellation, the proposed approach is shown to achieve guaranteed end-to-end delay bound and decrease packet loss ratio with better throughput, which is especially suitable for data transferring in case of high traffic load. Moreover, results of the complexity analysis demonstrate that LBRP-MA can have low onboard signaling, storage and computation requirements. Furthermore, issues of LBRP-MA such as ISL congestion index and cost modification factor are discussed.

Short-term traffic forecasting based on principal component analysis and a generalized regression neural network for satellite networks

With the rapid growth of satellite traffic, the ability to forecast traffic loads becomes vital for improving data transmission efficiency and resource management in satellite networks. To precisely forecast the short-term traffic loads in satellite networks, a forecasting algorithm based on principal component analysis and a generalized regression neural network （PCA-GRNN） is proposed. The PCA-GRNN algorithm exploits the hidden regularity of satellite networks and fully considers both the temporal and spatial correlations of satellite traffic. Specifically, it selects optimal time series of spatio-temporally correlated historical traffic from satellites as forecasting inputs and applies principal component analysis to reduce the input dimensions while preserving the main features of the data. Then, a generalized regression neural network is utilized to perform the final short-term load forecasting based on the obtained principal components. The PCA-GRNN algorithm is evaluated based on real-world traffic traces, and the results show that the PCA-GRNN method achieves a higher forecasting accuracy, has a shorter training time and is more robust than other state-of-the-art algorithms, even for incomplete traffic datasets. Therefore, the PCA- GRNN algorithm can be regarded as a preferred solution for use in real-time traffic forecasting for realistic satellite networks.

A Time and Space-based Dynamic IP Routing in Broadband Satellite Networks

The topology architecture, characteristics and routing technologies ofbroadband satellite net-works are studied in this paper. The authors propose the routing scheme ofsatellite networks and design a time and space-based distributed routing algorithm whose complexityis O (1). Simulation results aiming at satellite mobility show that the new algorithm can determinethe minimum propagation delay paths effectively.

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.

Joint Computing and Communication Resource Allocation for Satellite Communication Networks with Edge Computing

Benefit from the enhanced onboard processing capacities and high-speed satellite-terrestrial links,satellite edge computing has been regarded as a promising technique to facilitate the execution of the computation-intensive applications for satellite communication networks(SCNs).By deploying edge computing servers in satellite and gateway stations,SCNs can achieve significant performance gains of the computing capacities at the expense of extending the dimensions and complexity of resource management.Therefore,in this paper,we investigate the joint computing and communication resource management problem for SCNs to minimize the execution latency of the computation-intensive applications,while two different satellite edge computing scenarios and local execution are considered.Furthermore,the joint computing and communication resource allocation problem for the computation-intensive services is formulated as a mixed-integer programming problem.A game-theoretic and many-to-one matching theorybased scheme(JCCRA-GM)is proposed to achieve an approximate optimal solution.Numerical results show that the proposed method with low complexity can achieve almost the same weight-sum latency as the Brute-force method.