Satellite Nodes Research Articles

DRL-Based Load-Balancing Routing Scheme for 6G Space–Air–Ground Integrated Networks

Due to the rapid development of the space–air–ground integrated network (SAGIN), a satellite communication system has the advantages of wide coverage and low requirements for a geographical environment and is gradually becoming the main competitive technology for 6G. The low-earth-orbit (LEO) satellite network has the characteristics of low transmission delay, small propagation loss, and global coverage, and its exploration has become the main research object of contemporary satellite communications. However, traditional routing algorithms cannot adapt to the characteristics of the high dynamics and load-balancing requirements of LEO satellite networks. In this paper, a load-balancing routing algorithm for LEO satellites based on Deep Q-Network (DQN-LLRA) is proposed by using deep reinforcement learning. Making use of the model obtained by the DQN training, satellite nodes can select the best routing results according to the delay, bandwidth, and queue utilization of the surrounding satellite nodes. The simulation and analysis show that the path load obtained by the proposed algorithm is low. Compared with the Q-learning-based algorithm, this algorithm reduces the maximum queue utilization rate of the routing path by 5%, reduces the average queue utilization rate of the routing path by 13%, and effectively balances the load in the network.

SRAKN: Secure Roaming Authentication and Key Negotiation protocol for Space Information Network

Nowadays, the Space Information Network (SIN) with global signal coverage, large capacity, bandwidth-on-demand flexibility, and multiple services has attracted more and more users to enjoy real-time services without geographical restrictions. However, due to the openness of the satellite-to-ground wireless link, users are vulnerable to various attacks when accessing SIN and obtaining subscription services, which may pose security threats to user privacy, message integrity and confidentiality. Although many authentication protocols have been proposed to protect the security of access and data transmission, most of them are only applicable to the authentication scenarios in the home domain, without considering the security threats of roaming authentication in foreign domains. Moreover, the existing roaming authentication protocols still have some defects such as security vulnerabilities, long authentication delay, and high communication overhead. Therefore, in this paper, we propose a new secure roaming authentication and key negotiation protocol based on elliptic curve cryptography (ECC) for SIN, namely the SRAKN protocol, which also supports conditional anonymity and batch verification. In the roaming authentication phase, not only fast and low-overhead mutual authentication between the roaming user, satellite node and the foreign terrestrial control station (FTCS) is realized, but also a secure session key is jointly negotiated by the roaming user and FTCS to protect the subscription service transmission. The results of security analysis and performance comparison show that the SRAKN protocol is not only secure and resistant to various known attacks, but also has shorter roaming authentication delay, lower batch authentication overhead and communication overhead, making it more suitable for roaming users to access SIN in foreign domains.

A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing

Satellite edge computing (SEC) has garnered significant attention for its potential to deliver services directly to users. However, the uneven distribution of receiving tasks among satellites in the constellation can lead to uneven utilization of computing resources. This paper proposes a task offloading strategy for SEC that aims to minimize the average delay and energy consumption of tasks by assigning them to appropriate satellite nodes. The approach uses Lyapunov optimization to convert the long-term optimization problem with task queue length constraints into an assignment problem within a single time slot and solve it based on the Hungarian algorithm. Experimental simulations have shown that the proposed algorithm performs better than other baseline algorithms.

Multi‐objective routing algorithms for low‐earth orbit satellite network

SummaryThe low‐earth orbit (LEO) satellite network, composed of a large number of satellite nodes, is a hot research topic at present. Due to the characteristics of the large‐scale LEO satellite network, such as many satellite nodes, short orbit period, large dynamic change of topology, and unstable link‐state, its communication quality of service (QoS) requirements are difficult to meet. Aiming at this problem, various factors that may affect data transmission are first analyzed. The network link selection problem is modeled as a multi‐constraint optimization decision problem, a routing mathematical model based on linear programming (LP) is designed, and its solution is solved. Aiming at the problem of limited onboard computing resources, a multi‐object optimization Dijkstra algorithm (MOODA) is designed. The MOODA finds the optimal path according to the comprehensive performance of the link. It solves the problems of poor comprehensive QoS performance and the low degree of load balancing of the paths found by the Dijkstra algorithm. The simulation results show that the paths found by the two algorithms have good QoS, robustness, and load balancing performance.

Airborne Quantum Key Distribution Performance Analysis under Supersonic Boundary Layer.

Airborne quantum key distribution (QKD) that can synergize with terrestrial networks and quantum satellite nodes is expected to provide flexible and relay links for the large-scale integrated communication network. However, the photon transmission rate would be randomly reduced, owing to the random distributed boundary layer that surrounding to the surface of the aircraft when the flight speed larger than Mach 0.3. Here, we investigate the airborne QKD performance with the BL effects. Furthermore, we take experimental data of supersonic BL into the model and compare the airborne QKD performance under different conditions. Simulation results show that, owing to the complex small-scale turbulence structures in the supersonic boundary layer, the deflection angle and correspondingly drifted offset of the beam varied obviously and randomly, and the distribution probability of photons are redistributed. And the subsonic and supersonic boundary layer would decrease ~35.8% and ~62.5% of the secure key rate respectively. Our work provides a theoretical guidance towards a possible realization of high-speed airborne QKD.

PSEEMV: Provably secure and efficient emergency message verification scheme based on ECC and CRT for Space Information Network

With the rapid development of satellite computing and communication capabilities, Space Information Network (SIN) makes it possible for users to obtain subscription services anywhere and anytime. Especially for environmental monitoring, emergency rescue and disaster warning in remote areas, the deployed device cannot rely on traditional ground networks but can only transmit data and delay-sensitive emergency messages through satellite nodes of SIN. However, because of the openness of the satellite-to-ground link, it is vulnerable to various forms of attacks, which poses threats to data, emergency messages and user privacy. Existing mutual authentication schemes are not suitable for the transmission and verification of time-sensitive emergency messages due to high delay and computational overhead. Therefore, in this paper, we propose a provably secure and efficient emergency message verification scheme based on ECC and CRT, namely the PSEEMV scheme. The PSEEMV scheme not only uses the conditional privacy-preserving pseudo-identity to ensure privacy and accountability, but also supports confidential transmission and batch verification of emergency messages. Security proof and analysis prove that the PSEEMV scheme can resist forgery signature attacks based on the random oracle model, and meet the security and privacy requirements. Furthermore, performance analysis and comparison shows that the PSEEMV scheme has higher security, lower signaling, computational and communication overhead, and is more suitable for the transmission and verification of emergency message in the resource-constrained SIN.

Multi-Functional Time Expanded Graph: A Unified Graph Model for Communication, Storage, and Computation for Dynamic Networks Over Time

Space-air-ground integrated network (SAGIN) aided multi-tier computing network can be modelled as a dynamic and predictable network. For the SAGIN aided multi-tier computing network, the traditional time expanded graph (TEG) can only jointly model communication and storage capability, as well as one computing function for one mission flow within one same node. However, for multiple computing functions for one mission flow in one same node, TEG is not applicable. In this paper, for SAGIN aided multi-tier computing networks, we propose an multi-functional time expanded graph (MF-TEG) to jointly model the communication, storage, and computation capability of nodes where multiple computing functions for one mission flow in one same node can be characterized. Specifically, based on TEG, for each node having computation functions, we adopt the virtual network graph (VNG) to virtually decompose it into three virtual components: sub-virtual node, virtual computing nodes, and virtual transmission links, where the virtual computing node provides the computing function. We characterize the amount of data flow on each link and also present four kinds of fundamental constraints for the data flow in the MF-TEG for joint communication, storage, and computing function: computation capacity constraints, communication capacity constraints, storage capacity constraints, and flow conservation constraints. We provide one example of using MF-TEG to model the SAGIN aided multi-tier computing network with a service function chain (SFC), where satellite nodes could provide communication, storage, and multiple computing functions for one mission flow in one same node, where TEG is not valid. Furthermore, simulation results show that for SAGIN aided multi-tier computing network, the proposed MF-TEG model significantly outperforms the snapshot graph-aided VNG (SSG-aided VNG) model. The reason for that is only communication and computation capability is considered by the SSG-aided VNG model, while storage capability is not exploited.

Delay-Packet-Loss-Optimized Distributed Routing Using Spiking Neural Network in Delay-Tolerant Networking

Satellite communication is inevitable due to the Internet of Everything and the exponential increase in the usage of smart devices. Satellites have been used in many applications to make human life safe, secure, sophisticated, and more productive. The applications that benefit from satellite communication are Earth observation (EO), military missions, disaster management, and 5G/6G integration, to name a few. These applications rely on the timely and accurate delivery of space data to ground stations. However, the channels between satellites and ground stations suffer attenuation caused by uncertain weather conditions and long delays due to line-of-sight constraints, congestion, and physical distance. Though inter-satellite links (ISLs) and inter-orbital links (IOLs) create multiple paths between satellite nodes, both ISLs and IOLs have the same issues. Some essential applications, such as EO, depend on time-sensitive and error-free data delivery, which needs better throughput connections. It is challenging to route space data to ground stations with better QoS by leveraging the ISLs and IOLs. Routing approaches that use the shortest path to optimize latency may cause packet losses and reduced throughput based on the channel conditions, while routing methods that try to avoid packet losses may end up delivering data with long delays. Existing routing algorithms that use multi-optimization goals tend to use priority-based optimization to optimize either of the metrics. However, critical satellite missions that depend on high-throughput and low-latency data delivery need routing approaches that optimize both metrics concurrently. We used a modified version of Kleinrock's power metric to reduce delay and packet losses and verified it with experimental evaluations. We used a cognitive space routing approach, which uses a reinforcement-learning-based spiking neural network to implement routing strategies in NASA's High Rate Delay Tolerant Networking (HDTN) project.

A hybrid caching strategy for information-centric satellite networks based on node classification and popular content awareness

Low Earth Orbit (LEO) satellite networks are inexpensive to deploy and have wide coverage, and are widely available for differentiated content distribution services. With the development of on-board storage and computing capabilities, in-network caching technology in information centric networking (ICN) has proven to be an effective way to increase the throughput and content distribution efficiency of satellite networks. However, traditional caching and distribution schemes, which do not take into account the high-speed motion of satellite nodes and dynamic changes in topology, are not applicable to satellite networks. To address these issues, a hybrid caching strategy for satellite networks based on node classification and popular content awareness (NCPCA) is proposed. Firstly, the time slot partitioning method based on interlayer similarity is proposed to transform the dynamically changing process into a set of time slots with stable topology. Next, the satellite nodes are dynamically divided into two categories by fully considering the changes in connection relationships and interaction order during the spatial–temporal evolution of the satellite nodes. Nodes with satellite topological and functional characteristics are screened out as core nodes with TOPSIS algorithm, and the remaining nodes are regarded as edge nodes. Finally, a probabilistic caching scheme based on content popularity is adopted with core nodes as caching nodes to ensure caching performance and promote the diversity of cached content. Simulation results show that this strategy can effectively improve cache hit rate, reduce user request delay and content fetching hops, and promote the stable operation of satellite networks compared with other caching strategies.

Deep Learning-Enabled File Popularity-Aware Caching Replacement for Satellite-Integrated Content-Centric Networks

In recent years, satellite-integrated content centric networking (SCCN) has become an important solution for the future network with excellent bandwidth savings and file distribution capability in a wide range by the intrinsic caching function. At present, however, the high-speed changes of satellite network topology and coverage make it difficult to predict the file popularity of SCCN network, resulting in a low timeliness. This will lead to low node cache efficiency and bad data distribution performance. To address this issue, in this article, we have proposed a deep learning-enabled file popularity-aware caching replacement mechanism to achieve efficient file distribution in SCCN. In the proposed mechanism, we have developed a virtual location division scheme to keep the return path of content data invariable by remapping the time-varying topology of network into a static topology with virtual nodes. Furthermore, we have put forward a minimum delay file-caching set algorithm to predict the popularity of files in the proposed SCCN via a well-designed deep learning framework, which can find those high-popularity files most worthy of caching. The simulation results verified the proposed method can obviously degrade the access delay of all users and the cache hit ratio of satellite nodes, compared with current strategies, i.e., cache everything everywhere with least recently used, probCache, content-aware placement and discovery, and replacement (APDR), respectively.

Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack

Low Earth orbit satellite constellation networks (LSCNs) have attracted significant attention around the world due to their great advantages of low latency and wide coverage, but they also bring new challenges to network security. Distributed denial of service (DDoS) attacks are considered one of the most threatening attack methods in the field of Internet security. In this paper, a space-time graph model is built to identify the key nodes in LSCNs, and a DDoS attack is adopted as the main means to attack the key nodes. The scenarios of two-satellite-key-node and multi-satellite-key-node attacks are considered, and their security performance against DDoS attacks is also analyzed. The simulation results show that the transmission path of key satellite nodes will change rapidly after being attacked, and the average end-to-end delay and packet loss are linearly related to the number of key-node attacks. This work provides a comprehensive analysis of the security performance of LSCNs under a DDoS attack and theoretical support for future research on anti-DDoS attack strategies for LSCNs.

Age-Driven Spatially Temporally Correlative Updating in the Satellite-Integrated Internet of Things via Markov Decision Process

In this article, we consider the data updating problem in the Satellite-integrated Internet of Things network for the time-critical scenarios, i.e., animal tracking and environmental monitoring. Due to the limited channel rate during contact of transmission, however, constantly updating data with huge volume over the uplink will incur obvious waiting and transmission delay bringing stale information to the satellite node. To address this issue, we propose a novel metric, spatially temporally correlative mutual information (STI), to characterize the information timeliness from perspective of information entropy by considering the correlations between the last update message and the status of the information source. By maximizing the averaged STI, we find the optimal allocation policy of channel slots with a fixed updating period by formulating the problem as a Markov decision process (MDP) with possibly infinite state space. Furthermore, we derive the optimal amounts of allocated time slots in a unit frame by solving a constrained range integer optimization problem with respect to the average STI. The simulation results show that the proposed periodically updating policy can significantly improve the information freshness compared with the original slot allocation strategy and current commonly used scheduled access strategies, i.e., slotted ALOHA and Threshold-ALOHA.

Research on Routing Equalization Algorithm of Inter-Satellite Partition for Low-Orbit Micro-Satellites

Low-orbit micro-satellite technology has developed rapidly in recent years due to its advantages of low time delay, low cost and short research period. However, among the existing inter-satellite routing algorithms, the classical flooding and greedy algorithms and their derivatives also have some limitations. The path delay calculated by the flooding algorithm is small but the calculation is large, while the greedy algorithm is the opposite. In this paper, a balanced inter-satellite routing algorithm based on partition routing is proposed. This paper presents the simulation experiments for the following indexes of the classic inter-satellite routing algorithms and the balanced partition routing algorithm: computation complexity, single-node computation pressure, routing path delay, path delay variance (data in Topo table satisfy μ =5, σ2=10). The results reveal that the balanced partition routing algorithm achieves better performance. In this paper, two optimization directions of the balanced partition routing algorithm are simulated under conditions that the data in the Topo table satisfy μ =5, σ2= 6, σ2=10 and σ2=15, respectively, when comparing their performance indicators. The experiments show that these two optimization methods can be adapted to various application scenarios and can further reduce the hardware cost of satellite nodes.

Performance Analysis of Cooperative Nonorthogonal Multiple Access Scheme in Two-Layer GEO/LEO Satellite Network

Multilayer satellite networks (MLSNs) are viewed as a promising framework of the future satellite communication networks by realizing cooperative communication among satellites in different orbits. In this article, we introduce the cooperative nonorthogonal multiple access (C-NOMA) scheme into a two-layer geostationary Earth orbit/low Earth orbit satellite network in a frequency coexistence scenario and investigate the performance of the considered network. Taking into account the dynamic properties and channel characteristic of the satellite nodes, we first establish the generic architecture and then obtain the equivalent end-to-end signal-to-interference-plus-noise ratios along with the feasible region of power allocation coefficients based on the C-NOMA principle. Furthermore, the analytical expressions of the system ergodic capacity and outage probability are derived and approximated using Meijer-G functions to evaluate the system performance efficiently. Finally, simulation results are provided to attest the validity of the theoretical results as well as show the superiority of the C-NOMA scheme and the impact of various parameters on the system performance.

Dynamic Cache Replacement Strategy of Space Information Network Based on Cache Value

With the development of space network communication technology, satellite cache becomes possible. However, due to the limited space on the satellites, content replacement has become a major factor affecting network performance. Aiming at the limited cache resources of space network nodes and the low cache hit rate of nodes, a space information network dynamic cache replacement strategy (SINDR) based on cache value is proposed. On the basis of considering the popularity of the content and the cost of caching, the strategy also calculates the caching value of the content by quantifying the entropy increase of the node, and assigns a reasonable weight to each factor according to the Analytic Hierarchy Process. The cache value function is reconstructed; finally, the content of the virtual node and the physical satellite node that will serve the area is replaced by combining the characteristics of the space network, so as to obtain the optimal utilization of satellite cache resources. The simulation results show that, compared with the existing cache replacement strategies, the cache replacement strategy proposed in this paper can effectively improve the average cache hit rate and reduce the average user acquisition delay in the space information network environment.

Research on distributed beamforming synchronization technology in inter-satellite link system

Limited by satellite load and carrying capacity, it is difficult for inter-satellite link systems to improve information transmission capabilities by increasing power and antenna size like traditional terrestrial communication systems. Satellite communication payload power has always been an important limitation of long-distance communication performance. In order to improve the satellite’s long-distance communication capability and comprehensively consider the characteristics of Doppler frequency changes in the satellite environment, we propose a cooperative communication algorithm based on weak bit feedback. This algorithm can realize the time and space focusing of the beams emitted by multiple distributed satellite nodes at the destination node, and improve the performance of satellite long-distance communication. This paper simulates the above algorithm based on STK/Matlab software. The simulation results show that the weak bit feedback algorithm has stronger adaptability, faster convergence speed and higher synthesis efficiency under different channel changes. The algorithm is simple and easy to implement, and is suitable for the inter-satellite link environment with many satellite nodes and high scalability. Finally, we conducted hardware experimental verification, analyzed the signal flow of each module in the hardware, and tested it on the USRP software radio platform. The test results show that it is a feasible solution to realize distributed satellite node cooperative communication based on the weak bit feedback algorithm.

Secure routing for LEO satellite network survivability

Secure routing algorithm plays an important role in the performance of LEO satellite network survivability. However, due to the instability, openness and exposure of inter-satellite links, the internal routing of LEO satellite network is vulnerable to malicious attacks. Considering that trust management has a better performance in solving the internal attacks, this paper proposes a secure routing algorithm based on node trust for LEO satellite network, called SLT. The SLT is based on the distributed trust evaluation model, which is used to calculate the direct trust, indirect trust and aggregate trust value between satellite nodes through D-S evidence theory. Then, combining the low cost OPSPF routing protocol with the trust evaluation, the SLT algorithm is designed. Through timely detection and isolation of malicious nodes in the satellite network, STL algorithm can reduce the impact of malicious packet dropping caused by internal attacks. Finally, the simulations verifies that SLT algorithm has an average increase of 27% in packet delivery rate and an average decrease of 70% in packet loss rate, compared with OPSPF.

Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links

Constellation configuration design is a prerequisite and critical step in the construction of a mega-constellation system in low Earth orbit. However, the huge number of satellites and the intricate changes in relative positions among them make the configuration design the most challenging part. In this paper, we propose a configuration design scheme for mega-constellations considering collision-avoidance constraints with the objective of uniform global coverage. In this design scheme, the constellation is made up of multiple Walker constellations with the same orbital altitude and different orbital inclination. Moreover, the analytical expression for the minimum distance between any two satellites in the same orbital altitude is derived, and the constellation internal collision-avoidance constraint is established accordingly. Finally, a permanent inter-satellite link design scheme without dynamic reconstruction is presented based on the mega-constellation configuration. Simulation results show that the mega-constellation design scheme introduced in this paper can achieve relatively uniform global coverage (its N Asset Coverage ranges from 18 to 25). The mixed Walker constellation is capable of providing a greater number of N Asset Coverage for most of the world than the Walker constellation of the same satellite order of magnitude. In addition, the inter-satellite link scheme designed in this paper can ensure continuous and stable communication between any satellite nodes.

StarSum: A Star Architecture Based Model for Extractive Summarization

Extractive summarization aims to produce a concise summary while retaining the key information through the way of selecting sentences from the original document. Under such background, learning inter-sentence relations has hitherto been the issue of most concern. In this study, we propose a Star architecture based model for extractive summarization (StarSum), that takes advantage of self-attention strategy based Transformer and star-shaped structure, models sentences within a document as satellite nodes and introduces a virtual star node, constructs a star model for each document to learn inter-sentence relations. Based on the constructed star-shaped model, we further develop two sentence representation learning algorithms, namely star guiding satellite (SGS) algorithm and star incorporating satellite (SIS) algorithm, in order to extract summary-worthy sentences. Experimental results on CNN/Daily Mail, New York Times (NYT) and XSum datasets prove that StarSum model achieves advanced performance for extractive summarization and has comparable performance to the state-of-the-art extractive summarization model. The results also demonstrate that the SIS algorithm is more effective than the SGS algorithm.

Radiomics analysis of pretreatment MRI in predicting tumor response and outcome in hepatocellular carcinoma with transarterial chemoembolization: a two-center collaborative study.

To develop a machine-learning model by integrating clinical and imaging modalities for predicting tumor response and survival of hepatocellular carcinoma (HCC) with transarterial chemoembolization (TACE). 140 HCC patients with TACE were retrospectively included from two centers. Tumor response were evaluated using modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria. Response-related radiomics scores (Rad-scores) were constructed on T2-weighted images (T2WI) and dynamic contrast-enhanced (DCE) imaging separately, and then integrated with conventional clinic-radiological variables into a logistic regression (LR) model for predicting tumor response. LR model was trained in 94 patients in center 1 and independently tested in 46 patients in center 2. Among 4 MRI sequences, T2WI achieved better performance than DCE (area under the curve [AUC] 0.754 vs 0.602 to 0.752). LR model by combining Rad-score on T2WI with Barcelona Clinic Liver Cancer (BCLC) stage and albumin-bilirubin (ALBI) grade resulted in an AUC of 0.813 in training and 0.781 in test for predicting tumor response. In survival analysis, progression-free survival (PFS) and overall survival (OS) presented significant difference between LR-predicted responders and non-responders. The ALBI grade and BCLC stage were independent predictors of PFS; and LR-predicted response, ALBI grade, satellite node, and BCLC stage were independent predictors of OS. The resulting Cox model produced concordance-indexes of 0.705 and 0.736 for predicting PFS and OS, respectively. The model combined MRI radiomics with clinical factors demonstrated favorable performance for predicting tumor response and clinical outcomes, thus may help personalized clinical management.