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    BRAIN-COMPUTER-INTERFACE INSPIRED COMMUNICATIONS
  • BRAIN-COMPUTER-INTERFACE INSPIRED COMMUNICATIONS
    A User-Friendly SSVEP-Based BCI Using Imperceptible Phase-Coded Flickers at 60Hz
    Lu Jiang, Weihua Pei, Yijun Wang
    2022, 19(2): 1-14.
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    A brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) was developed by four-class phase-coded stimuli. SSVEPs elicited by flickers at 60Hz, which is higher than the critical fusion frequency (CFF), were compared with those at 15Hz and 30Hz. SSVEP components in electroencephalogram (EEG) were detected using task related component analysis (TRCA) method. Offline analysis with 17 subjects indicated that the highest information transfer rate (ITR) was 29.80±4.65bpm with 0.5s data length for 60Hz and the classification accuracy was 70.07±4.15%. The online BCI system reached an averaged classification accuracy of 87.75±3.50% at 60Hz with 4s, resulting in an ITR of 16.73±1.63bpm. In particular, the maximum ITR for a subject was 80bpm with 0.5s at 60Hz. Although the BCI performance of 60Hz was lower than that of 15Hz and 30Hz, the results of the behavioral test indicated that, with no perception of flicker, the BCI system with 60Hz was more comfortable to use than 15Hz and 30Hz. Correlation analysis revealed that SSVEP with higher signal-to-noise ratio (SNR) corresponded to better classification performance and the improvement in comfortableness was accompanied by a decrease in performance. This study demonstrates the feasibility and potential of a user-friendly SSVEP-based BCI using imperceptible flickers.
  • BRAIN-COMPUTER-INTERFACE INSPIRED COMMUNICATIONS
    Steady-State Visual Evoked Potential (SSVEP) in a New Paradigm Containing Dynamic Fixation Points
    Yuang Li, Yong Ge, Xuefei Zhong, Xiong Zhang
    2022, 19(2): 15-30.
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    Steady-state visual evoked potential (SSVEP) has become a powerful tool for Brain Computer Interface (BCI) because of its high signal-tonoise ratio, high information transmission rate,and minimal user training.At present, the edge information of each region cannot be identified in spatial coding based on SSVEP-BCI technology, and the user experience is poor. To solve this problem, this paper designed a new paradigm to explore the relationship between the fixation point position of continuous sliding and the correlation coefficient ratio in the dualfrequency case. Firstly, the standard sinusoidal signal was employed to simulate the Electroencephalogram (EEG) signal, which verified the reliability of characterizing the amplitude variation of test signal by correlation coefficient.Then, the relationship between the amplitude response of SSVEP and the distance between the fixation point and the stimulus in the horizontal direction was tested by Canonical Correlation Analysis (CCA) and Filter bank CCA (FBCCA). Finally, the experimental data were offline analyzed under the condition of continuous sliding of the fixation point. It is feasible and reasonable to detect the amplitude change of frequency component in SSVEP by utilizing the spatial coding method in this paper to improve the extraction accuracy of spatial information.
  • BRAIN-COMPUTER-INTERFACE INSPIRED COMMUNICATIONS
    Toward a Neurophysiological Measure of Image Quality Perception Based on Algebraic Topology Analysis
    Chang Liu, Xiaoyu Ma, Yijie Zhou, Jiaojiao Wang, Dingguo Yu
    2022, 19(2): 31-38.
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    The bandwidth of internet connections is still a bottleneck when transmitting large amounts of images, making the image quality assessment essential. Neurophysiological assessment of image quality has highlight advantages for it does not interfere with natural viewing behavior. However, in JPEG compression, the previous study is hard to tell the difference between the electroencephalogram (EEG) evoked by different quality images. In this paper, we propose an EEG analysis approach based on algebraic topology analysis, and the result shows that the difference between Euler characteristics of EEG evoked by different distortion images is striking both in the alpha and beta band. Moreover, we further discuss the relationship between the images and the EEG signals, and the results implied that the algebraic topological properties of images are consistent with that of brain perception, which is possible to give birth to braininspired image compression based on algebraic topological features. In general, an algebraic topologybased approach was proposed in this paper to analyze the perceptual characteristics of image quality, which will be beneficial to provide a reliable score for data compression in the network and improve the network transmission capacity.
  • BRAIN-COMPUTER-INTERFACE INSPIRED COMMUNICATIONS
    Transfer Learning Algorithm Design for Feature Transfer Problem in Motor Imagery Brain-computer Interface
    Yu Zhang, Huaqing Li, Heng Dong, Zheng Dai, Xing Chen, Zhuoming Li
    2022, 19(2): 39-46.
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    The non-stationary of the motor imagery electroencephalography(MI-EEG) signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI). The nonstationary of the MI-EEG signal and the changes of the experimental environment make the feature distribution of the testing set and training set deviates, which reduces the classification accuracy of MI-BCI. In this paper, we propose a Kullback-Leibler divergence (KL) -based transfer learning algorithm to solve the problem of feature transfer, the proposed algorithm uses KL to measure the similarity between the training set and the testing set, adds support vector machine (SVM) classification probability to classify and weight the covariance, and discards the poorly performing samples. The results show that the proposed algorithm can significantly improve the classification accuracy of the testing set compared with the traditional algorithms, especially for subjects with medium classification accuracy. Moreover, the algorithm based on transfer learning has the potential to improve the consistency of feature distribution that the traditional algorithms do not have, which is significant for the application of MI-BCI.
  • BRAIN-COMPUTER-INTERFACE INSPIRED COMMUNICATIONS
    Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition
    Miao Shi, Chao Wang, Wei Zhao, Xinshi Zhang, Ye Ye, Nenggang Xie
    2022, 19(2): 47-61.
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    Ocular artifacts in Electroencephalography (EEG) recordings lead to inaccurate results in signal analysis and process. Variational Mode Decomposition (VMD) is an adaptive and completely nonrecursive signal processing method. There are two parameters in VMD that have a great influence on the result of signal decomposition. Thus, this paper studies a signal decomposition by improving VMD based on squirrel search algorithm (SSA). It's improved with abilities of global optimal guidance and opposition based learning. The original seasonal monitoring condition in SSA is modified. The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions. Opposition-based learning is introduced to reposition the position of the population in this stage. It is applied to optimize the important parameters of VMD. GOSSA-VMD model is established to remove ocular artifacts from EEG recording. We have verified the effectiveness of our proposal in a public dataset compared with other methods. The proposed method improves the SNR of the dataset from -2.03 to 2.30.
  • BRAIN-COMPUTER-INTERFACE INSPIRED COMMUNICATIONS
    BCI+VR Rehabilitation Design of Closed-Loop Motor Imagery Based on the Degree of Drug Addiction
    Xuelin Gu, Banghua Yang, Shouwei Gao, Honghao Gao, Linfeng Yan, Ding Xu, Wen Wang
    2022, 19(2): 62-72.
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    After abusing drugs for long, drug users will experience deteriorated self-control cognitive ability, and poor emotional regulation. This paper designs a closed-loop virtual-reality (VR), motorimagery (MI) rehabilitation training system based on brain-computer interface (BCI) (MI-BCI+VR), aiming to enhance the self-control, cognition, and emotional regulation of drug addicts via personalized rehabilitation schemes. This paper is composed of two parts. In the first part, data of 45 drug addicts (mild: 15; moderate: 15; and severe: 15) is tested with electroencephalogram (EEG) and near-infrared spectroscopy (NIRS) equipment (EEG-NIRS) under the dual-mode, synchronous signal collection paradigm. Using these data sets, a dual-modal signal convolutional neural network (CNN) algorithm is then designed based on decision fusion to detect and classify the addiction degree. In the second part, the MIBCI+ VR rehabilitation system is designed, optimizing the Filter Bank Common Spatial Pattern (FBCSP) algorithm used in MI, and realizing MI-EEG intention recognition. Eight VR rehabilitation scenes are devised, achieving the communication between MI-BCI and VR scene models. Ten subjects are selected to test the rehabilitation system offline and online, and the test accuracy verifies the feasibility of the system. In future, it is suggested to develop personalized rehabilitation programs and treatment cycles based on the addiction degree.
  • BRAIN-COMPUTER-INTERFACE INSPIRED COMMUNICATIONS
    E3GCAPS: Efficient EEG-Based Multi-Capsule Framework with Dynamic Attention for Cross-Subject Cognitive State Detection
    Yue Zhao, Guojun Dai, Xin Fang, Zhengxuan Wu, Nianzhang Xia, Yanping Jin, Hong Zeng
    2022, 19(2): 73-89.
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    Cognitive state detection using electroencephalogram (EEG) signals for various tasks has attracted significant research attention. However, it is difficult to further improve the performance of crosssubject cognitive state detection. Further, most of the existing deep learning models will degrade significantly when limited training samples are given, and the feature hierarchical relationships are ignored. To address the above challenges, we propose an efficient interpretation model based on multiple capsule networks for cross-subject EEG cognitive state detection, termed as Efficient EEG-based Multi-Capsule Framework (E3GCAPS). Specifically, we use a selfexpression module to capture the potential connections between samples, which is beneficial to alleviate the sensitivity of outliers that are caused by the individual differences of cross-subject EEG. In addition, considering the strong correlation between cognitive states and brain function connection mode, the dynamic subcapsule-based spatial attention mechanism is introduced to explore the spatial relationship of multi-channel 1D EEG data, in which multichannel 1D data greatly improving the training efficiency while preserving the model performance. The effectiveness of the E3GCAPS is validated on the Fatigue-Awake EEG Dataset (FAAD) and the SJTU Emotion EEG Dataset (SEED). Experimental results show E3GCAPS can achieve remarkable results on the EEG-based cross-subject cognitive state detection under different tasks.
    • COVER PAPER
  • COVER PAPER
    Space-Air-Ground Integrated Network (SAGIN) for 6G: Requirements, Architecture and Challenges
    Huanxi Cui, Jun Zhang, Yuhui Geng, Zhenyu Xiao, Tao Sun, Ning Zhang, Jiajia Liu, Qihui Wu, Xianbin Cao
    2022, 19(2): 90-108.
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    As the fifth-generation (5G) mobile communication network may not meet the requirements of emerging technologies and applications, including ubiquitous coverage, industrial internet of things (IIoT), ubiquitous artificial intelligence (AI), digital twins (DT), etc., this paper aims to explore a novel space-air-ground integrated network (SAGIN) architecture to support these new requirements for the sixth-generation (6G) mobile communication network in a flexible, low-latency and efficient manner. Specifically, we first review the evolution of the mobile communication network, followed by the application and technology requirements of 6G. Then the current 5G non-terrestrial network (NTN) architecture in supporting the new requirements is deeply analyzed. After that, we proposes a new flexible, low-latency and flat SAGIN architecture, and presents corresponding use cases. Finally, the future research directions are discussed.
    • COMMUNICATIONS THEORIES & SYSTEMS
  • COMMUNICATIONS THEORIES & SYSTEMS
    Sensitivity and Distance Based Performance Analysis for Batteryless Tags with Transmit Beamforming and Ambient Backscattering
    Minzheng Jia, Chaochao Yao, Wei Liu, Ruyi Ye, Tutun Juhana, Bo Ai
    2022, 19(2): 109-117.
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    Ambient backscatter is a new green technology for Internet of Things (IoT) that utilizes surrounding wireless signals to enable batteryless devices to communicate with other devices. The battery-free devices first harvest energy from ambient wireless signals and then backscatter the signals for communications. Clearly, sensitivity and distance are two important parameters for system performance. However, most existing studies on ambient backscatter communication systems do not consider the impact of the sensitivity of the energy-harvesting nodes and the distances between these devices. In this paper, we first provide a literature review for ambient communication technology and then take sensitivity and distance as two key parameters and investigate the sensitivity and distance based performance for ambient backscatter communication systems. Specifically, we establish the mathematical model based on distances between transceivers and backscattering nodes, extract a parameter that can differentiate the direct path and the backscattering path, evaluate the effects of transmit beamforming, design an energy detector for the reader, and analyze the outage probability of energy harvesting at the tag and the bit error rate (BER) at the reader. Simulations are then provided to corroborate the proposed studies.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Successive Interference Cancellation and Alignment in K-User MIMO Interference Channels with Partial Unidirectional Strong Interference
    Long Suo, Hongyan Li, Shun Zhang, Jiandong Li
    2022, 19(2): 118-130.
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    Recently cellular networks have been densely and heterogeneously deployed indoors and outdoors to expand the network capacity, and thus the in-building propagation loss and the transmit power diversity of access points will exacerbate link heterogeneity and result in partial unidirectional strong interference. To make full use of the strong interference feature, we propose the successive interference cancellation and alignment (SICA) scheme in the K-user interference channel with partial unidirectional strong interference. SICA is designed to transmit two kinds of data streams simultaneously, the alignment streams and superposition streams. The alignment streams will follow the interference alignment criterion to maintain the optimal degrees of freedom (DoF) performance; the superposition streams are handled via successive interference cancellation at all the strongly interfered receivers to improve the overall achievable rate. The joint transceiver designs for SICA is modeled as a weighted sum rate (WSR) maximization problem, and then can be alternately solved for a local optimum according to the optimality equivalence between WSR and its corresponding weighted mean square error (WMMSE) problem. Simulation results have confirmed the sum rate improvement and DoF optimality of the proposed SICA scheme.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Robust Trajectory and Communication Design for Angle-Constrained Multi-UAV Communications in the Presence of Jammers
    Yufang Gao, Yang Wu, Zhichao Cui, Wendong Yang, Guojie Hu, Shiming Xu
    2022, 19(2): 131-147.
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    This paper studies a multi-unmanned aerial vehicle (UAV) enabled wireless communication system, where multiple UAVs are employed to communicate with a group of ground terminals (GTs) in the presence of potential jammers. We aim to maximize the throughput overall GTs by jointly optimizing the UAVs' trajectory, the GTs' scheduling and power allocation. Unlike most prior studies, we consider the UAVs' turning and climbing angle constraints, the UAVs' three-dimensional (3D) trajectory constraints, minimum UAV-to-UAV (U2U) distance constraint, and the GTs' transmit power requirements. However, the formulated problem is a mixed-integer non-convex problem and is intractable to work it out with conventional optimization methods. To tackle this difficulty, we propose an efficient robust iterative algorithm to decompose the original problem be three sub-problems and acquire the suboptimal solution via utilizing the block coordinate descent (BCD) method, successive convex approximation (SCA) technique, and S-procedure. Extensive simulation results show that our proposed robust iterative algorithm offers a substantial gain in the system performance compared with the benchmark algorithms.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Design of a Microwave Filter Based on a Novel Negative Coupling Structure with Conical Through-Hole
    Shunliang Meng, Fei Liang, Wenzhong Lu, Zihang Lin, Yuhao Yin, Rong Zhang
    2022, 19(2): 148-157.
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    This article introduces the design theory of ceramic waveguide filter and proposes a new type of negative coupling structure with a conical throughhole, which has fine-adjustment of negative coupling without significantly increasing the insertion loss of the filter. Based on this, the article proposes an eightcavity ceramic waveguide filter design for 5G base stations. It also presents a detailed discussion on the influence of the cross-coupling slot lengths L2 and L4 on the transmission zeros positions during the filter optimization process and the relevant change rules. For the proposed optimized filter, the observed performance indicators include the center frequency of 3.5 GHz, working bandwidth of 200 MHz, an insertion loss of ≤ 2.0 dB, return loss of ≥ 19 dB, and out-of-band nearend suppression and out-of-band far-end suppression of ≥ 39 dB and ≥ 63 dB, respectively. The test performance results obtained for the sample, with structural parameters as per the simulation model, were in good agreement with the simulation results.
    • NETWORKS & SECURITY
  • NETWORKS & SECURITY
    A Security Enhancement Model Based on Switching Edge Strategy in Interdependent Heterogeneous Cyber-Physical Systems
    Dandan Zhao, Can Liu, Guangquan Xu, Zhiguo Ding, Hao Peng, Juan Yu, Jianmin Han
    2022, 19(2): 158-173.
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    With the advent of cross-domain interconnection, large-scale sensor network systems such as smart grids, smart homes, and intelligent transportation have emerged. These complex network systems often have a CPS (Cyber-Physical System) architecture and are usually composed of multiple interdependent systems. Minimal faults between interdependent networks may cause serious cascading failures between the entire system. Therefore, in this paper, we will explore the robustness detection schemes for interdependent networks. Firstly, by calculating the largest giant connected component in the entire system, the security of interdependent network systems under different attack models is analyzed. Secondly, a comparative analysis of the cascade failure mechanism between interdependent networks under the edge enhancement strategy is carried out. Finally, the simulation results verify the impact of system reliability under different handover edge strategies and show how to choose a better handover strategy to enhance its robustness. The further research work in this paper can also help design how to reduce the interdependence between systems, thereby further optimizing the interdependent network system's structure to provide practical support for reducing the cascading failures. In the later work, we hope to explore our proposed strategies in the network model of real-world or close to real networks.
  • NETWORKS & SECURITY
    Cryptomining Malware Detection Based on Edge Computing-Oriented Multi-Modal Features Deep Learning
    Wenjuan Lian, Guoqing Nie, Yanyan Kang, Bin Jia, Yang Zhang
    2022, 19(2): 174-185.
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    In recent years, with the increase in the price of cryptocurrencies, the number of malicious cryptomining software has increased significantly. With their powerful spreading ability, cryptomining malware can unknowingly occupy our resources, harm our interests, and damage more legitimate assets. However, although current traditional rule-based malware detection methods have a low false alarm rate, they have a relatively low detection rate when faced with a large volume of emerging malware. Even though common machine learning-based or deep learning-based methods have certain ability to learn and detect unknown malware, the characteristics they learn are single and independent, and cannot be learned adaptively. Aiming at the above problems, we propose a deep learning model with multi-input of multi-modal features, which can simultaneously accept digital features and image features on different dimensions. The model in turn includes parallel learning of three sub-models and ensemble learning of another specific sub-model. The four sub-models can be processed in parallel on different devices and can be further applied to edge computing environments. The model can adaptively learn multi-modal features and output prediction results. The detection rate of our model is as high as 97.01% and the false alarm rate is only 0.63%. The experimental results prove the advantage and effectiveness of the proposed method.
  • NETWORKS & SECURITY
    Secure Transmissions in Wireless Multiuser Networks Using Message Correlation
    Hongliang He, Libo Wang
    2022, 19(2): 186-200.
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    Due to the openness of wireless multiuser networks, the private information transmitted in uplink or downlink is vulnerable to eavesdropping. Especially, when the downlink transmissions use nonorthogonal multiple access (NOMA) techniques, the system further encounters interior eavesdropping. In order to address these security problems, we study the secret communication in multiuser networks with both uplink and downlink transmissions. Specifically, in uplink transmissions, the private messages transmitted in each slot are correlated, so any loss of the private information at the eavesdropper will prevent the eavesdropper from decoding the private information in later time slots. In downlink transmissions, the messages are correlated to the uplink information. In this way, any unexpected users who lose the expected user's uplink information cannot decode its downlink information. The intercept probability is used to measure security performance and we analyze it in theory. Finally, simulation results are provided to corroborate our theoretical analysis.
  • NETWORKS & SECURITY
    Outsourced Privacy-Preserving Anomaly Detection in Time Series of Multi-Party
    Chunkai Zhang, Wei Zuo, Peng Yang, Ye Li, Xuan Wang
    2022, 19(2): 201-213.
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    Anomaly detection has practical significance for finding unusual patterns in time series. However, most existing algorithms may lose some important information in time series presentation and have high time complexity. Another problem is that privacy-preserving was not taken into account in these algorithms. In this paper, we propose a new data structure named Interval Hash Table (IHTable) to capture more original information of time series and design a fast anomaly detection algorithm based on Interval Hash Table (ADIHT). The key insight of ADIHT is distributions of normal subsequences are always similar while distributions of anomaly subsequences are different and random by contrast. Furthermore, to make our proposed algorithm fit for anomaly detection under multiple participation, we propose a privacy-preserving anomaly detection scheme named OP-ADIHT based on ADIHT and homomorphic encryption. Compared with existing anomaly detection schemes with privacy-preserving, OP-ADIHT needs less communication cost and calculation cost. Security analysis of different circumstances also shows that OP-ADIHT will not leak the privacy information of participants. Extensive experiments results show that ADIHT can outperform most anomaly detection algorithms and perform close to the best results in terms of AUC-ROC, and ADIHT needs the least time.
    • EMERGING TECHNOLOGIES & APPLICATIONS
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Deep Learning-Based Prediction of Traffic Accidents Risk for Internet of Vehicles
    Haitao Zhao, Xiaoqing Li, Huiling Cheng, Jun Zhang, Qin Wang, Hongbo Zhu
    2022, 19(2): 214-224.
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    With the increasing number of vehicles, traffic accidents pose a great threat to human lives. Hence, aiming at reducing the occurrence of traffic accidents, this paper proposes an algorithm based on a deep convolutional neural network and a random forest to predict accident risks. Specifically, the proposed algorithm includes a feature extractor and a feature classifier, where the former extracts key features using a convolutional neural network and the latter outputs a probability value of traffic accidents using a random forest with multiple decision trees, which indicates the degree of accident risks. Simulations show that the proposed algorithm can achieve higher performance in terms of the Area Under the Curve (AUC) of the Receiver Characteristic Operator as well as accuracy than the existing algorithms based on the Adaboost or the pure convolutional neural networks.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    A Multi-Agent Based Model for User Interest Mining on Sina Weibo
    Meijia Wang, Qingshan Li
    2022, 19(2): 225-234.
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    User interest mining on Sina Weibo is the basis of personalized recommendations, advertising, marketing promotions, and other tasks. Although great progress has been made in this area, previous studies have ignored the differences among users: the varied behaviors and habits that lead to unique user data characteristics. It is unreasonable to use a single strategy to mine interests from such varied user data. Therefore, this paper proposes an adaptive model for user interest mining based on a multi-agent system whose input includes self-descriptive user data, microblogs and correlations. This method has the ability to select the appropriate strategy based on each user's data characteristics. The experimental results show that the proposed method performs better than the baselines.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Reliability Evaluation of All-User Terminals in LEO Satellite Communication Network Based on Modular Reduction
    Yangyang Du, Sifeng Liu, Zhigeng Fang, Su Gao
    2022, 19(2): 235-246.
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    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.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Load Balancing Routing Algorithm Based on Extended Link States in LEO Constellation Network
    Chaoying Dong, Xin Xu, Aijun Liu, Xiaohu Liang
    2022, 19(2): 247-260.
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    As an important part of satellite communication network, LEO satellite constellation network is one of the hot research directions. Since the nonuniform distribution of terrestrial services may cause inter-satellite link congestion, improving network load balancing performance has become one of the key issues that need to be solved for routing algorithms in LEO network. Therefore, by expanding the range of available paths and combining the congestion avoidance mechanism, a load balancing routing algorithm based on extended link states in LEO constellation network is proposed. Simulation results show that the algorithm achieves a balanced distribution of traffic load, reduces link congestion and packet loss rate, and improves throughput of LEO satellite network.