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    COVER PAPER
  • COVER PAPER
    Edge Caching in Blockchain Empowered 6G
    Wen Sun, Sheng Li, Yan Zhang
    2021, 18(1): 1-17.
    Abstract ( )   Knowledge map   Save
    The sixth generation (6G) of wireless cellular networks is expected to incorporate the latest developments in network infrastructure and emerging advances in technology. In the age of 6G, edge caching technology will evolve towards intelligence, dynamics, and security. However, the security problems of edge caching, including data tampering and eavesdropping, are seldomly considered in most literatures. In this paper, we consider the two-hop edge caching where the blockchain and physical layer security technologies are adopted to prevent data from being tampered with and eavesdropped. We design blockchain-based framework to guarantee the reliability of important data such as the frequency of contents and jointly optimize content caching probability and redundancy rate to maximize the secure transmission probability. Extensive simulation shows that our optimization scheme can significantly improve the secure transmission probability of edge cache network, whether facing the threat of independent eavesdropping or joint eavesdropping.
    • COMMUNICATIONS THEORIES & SYSTEMS
  • COMMUNICATIONS THEORIES & SYSTEMS
    Spectrum Prediction Based on GAN and Deep Transfer Learning: A Cross-Band Data Augmentation Framework
    Fandi Lin, Jin Chen, Guoru Ding, Yutao Jiao, Jiachen Sun, Haichao Wang
    2021, 18(1): 18-32.
    Abstract ( )   Knowledge map   Save
    This paper investigates the problem of data scarcity in spectrum prediction. A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes. The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency. Moreover, the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited. To address the above issues, we develop a cross-band data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network (GAN) and deep transfer learning. Firstly, through the similarity measurement, we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band. Then, through the data augmentation by feeding the small amount of the target data into the pre-trained GAN, temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN. Finally, experiment results demonstrate the effectiveness of the proposed framework.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Layered D2D NOMA
    Jinjuan Ju, Jinyuan Gu, Guan Zhang
    2021, 18(1): 33-42.
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    This paper investigates a layered power allocation (PA) non-orthogonal multiple access (NOMA) scheme for device-to-device (D2D) relaying networks, where the strategy of partial data transmission at relay nodes are adopted to improve the efficiency of resources. In addition, to satisfy different quality-of-service (QoS) requirements from multiple users, layered and grouped manners are involved. Moreover, the closed-form expressions in terms of the sum-rate (SR) and outage probability of the proposed scheme are derived for independent Rayleigh fading channels, which demonstrates our theoretical analysis. Both analytical and simulation results are provided to show the superiority of our proposed scheme compared with existing works.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Fully Connected Feedforward Neural Networks Based CSI Feedback Algorithm
    Ming Gao, Tanming Liao, Yubin Lu
    2021, 18(1): 43-48.
    Abstract ( )   Knowledge map   Save
    In modern wireless communication systems, the accurate acquisition of channel state information (CSI) is critical to the performance of beamforming, non-orthogonal multiple access (NOMA), etc. However, with the application of massive MIMO in 5G, the number of antennas increases by hundreds or even thousands times, which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme. In this paper, by using deep learning technology, we develop a system framework for CSI feedback based on fully connected feedforward neural networks (FCFNN), named CF-FCFNN. Through learning the training set composed of CSI, CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Erasure-Correction-Enhanced Iterative Decoding for LDPC-RS Product Codes
    Weigang Chen, Ting Wang, Changcai Han, Jinsheng Yang
    2021, 18(1): 49-60.
    Abstract ( )   Knowledge map   Save
    Low-density parity-check (LDPC) codes are widely used due to their significant error-correction capability and linear decoding complexity. However, it is not sufficient for LDPC codes to satisfy the ultra low bit error rate (BER) requirement of next-generation ultra-high-speed communications due to the error floor phenomenon. According to the residual error characteristics of LDPC codes, we consider using the high rate Reed-Solomon (RS) codes as the outer codes to construct LDPC-RS product codes to eliminate the error floor and propose the hybrid error-erasure-correction decoding algorithm for the outer code to exploit erasure-correction capability effectively. Furthermore, the overall performance of product codes is improved using iteration between outer and inner codes. Simulation results validate that BER of the product code with the proposed hybrid algorithm is lower than that of the product code with no erasure correction. Compared with other product codes using LDPC codes, the proposed LDPC-RS product code with the same code rate has much better performance and smaller rate loss attributed to the maximum distance separable (MDS) property and significant erasure-correction capability of RS codes.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Power Allocation for NOMA in D2D Relay Communications
    Yan Cai, Chunhua Ke, Yiyang Ni, Jun Zhang, Hongbo Zhu
    2021, 18(1): 61-69.
    Abstract ( )   Knowledge map   Save
    Non-orthogonal multiple access (NOMA) is considered as one of the key technologies for the fifth generation (5G) wireless communications. The integration of NOMA and device-to-device (D2D) communications has recently attracted wide attention. In this paper, a relaying D2D communications assisted with cooperative relaying systems using NOMA (DRC-NOMA) is considered. We analyze the ergodic sum-rate for the proposed system and then derive the closed-form expressions. In addition, an optimal power allocation strategy maximizing the ergodic sum-rate is proposed based on these analysis results. Numerical results show the good agreement between the results of analysis and Monte Carlo method. The proposed DRC-NOMA has a great improvement of the ergodic sum-rate in the small regime of average channel gain of D2D pair.
  • COMMUNICATIONS THEORIES & SYSTEMS
    A Game-Theoretic Perspective on Resource Management for Large-Scale UAV Communication Networks
    Jiaxin Chen, Ping Chen, Qihui Wu, Yuhua Xu, Nan Qi, Tao Fang
    2021, 18(1): 70-87.
    Abstract ( )   Knowledge map   Save
    As a result of rapid development in electronics and communication technology, large-scale unmanned aerial vehicles (UAVs) are harnessed for various promising applications in a coordinated manner. Although it poses numerous advantages, resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently. Specifically, due to the inherent requirements and future development trend, distributed resource management is suitable. In this article, we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner. By exploring the inherent features, the distinctive challenges are discussed. Then, we explore several game-theoretic models that not only combat the challenges but also have broad application prospects. We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks. Specifically, mean-field game, graphical game, Stackelberg game, coalition game and potential game are included. After that, we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models. Finally, we give some future research directions to shed light on future opportunities and applications.
    • NETWORKS & SECURITY
  • NETWORKS & SECURITY
    Joint Trajectory and Power Optimization for Securing UAV Communications Against Active Eavesdropping
    Bin Duo, Junsong Luo, Yilian Li, Hao Hu, Zibin Wang
    2021, 18(1): 88-99.
    Abstract ( )   Knowledge map   Save
    Due to both of jamming and eavesdropping, active eavesdroppers can induce more serious security threats to unmanned aerial vehicle (UAV)-enabled communications. This paper considers a secure UAV communication system including both the downlink (DL) and uplink (UL) transmissions, where the confidential information is transmitted between a UAV and a ground node in the presence of an active eavesdropper. We aim to maximize the average secrecy rates of the DL and UL communications, respectively, by jointly optimizing the UAV trajectory and the UAV/ground node’s transmit power control over a given flight period. Due to the non-convexity of the formulated problems, it is difficult to obtain globally optimal solutions. However, we propose efficient iterative algorithms to obtain high-quality suboptimal solutions by applying the block coordinate descent and successive convex optimization methods. Simulation results show that the joint optimization algorithms can effectively improve the secrecy rate performance for both the DL and UL communications, as compared with other baseline schemes. The proposed schemes can be considered as special cases of UAV-assisted non-orthogonal multiple access (NOMA) networks.
  • NETWORKS & SECURITY
    Intelligent Immunity Based Security Defense System for Multi-Access Edge Computing Network
    Chengcheng Zhou, Yanping Yu, Shengsong Yang, Haitao Xu
    2021, 18(1): 100-107.
    Abstract ( )   Knowledge map   Save
    In this paper, the security problem for the multi-access edge computing (MEC) network is researched, and an intelligent immunity-based security defense system is proposed to identify the un-authorized mobile users and to protect the security of whole system. In the proposed security defense system, the security is protected by the intelligent immunity through three functions, identification function, learning function, and regulation function, respectively. Meanwhile, a three process-based intelligent algorithm is proposed for the intelligent immunity system. Numerical simulations are given to prove the effeteness of the proposed approach.
  • NETWORKS & SECURITY
    CNN-Based Intelligent Safety Surveillance in Green IoT Applications
    Wengang Cao, Jianing Zhang, Changxin Cai, Quan Chen, Yu Zhao, Yimo Lou, Wei Jiang, Guan Gui
    2021, 18(1): 108-119.
    Abstract ( )   Knowledge map   Save
    Safety surveillance is considered one of the most important factors in many constructing industries for green internet of things (IoT) applications. However, traditional safety monitoring methods require a lot of labor source. In this paper, we propose intelligent safety surveillance (ISS) method using a convolutional neural network (CNN), which is an auto-supervised method to detect workers whether or not wearing helmets. First, to train the CNN-based ISS model, the labeled datasets mainly come from two aspects: 1) our labeled datasets with the full labeled on both helmet and pedestrian; 2) public labeled datasets with the parts labeled either on the helmet or pedestrian. To fully take advantage of all datasets, we redesign CNN structure of network and loss functions based on YOLOv3. Then, we test our proposed ISS method based on the specific detection evaluation metrics. Finally, experimental results are given to show that our proposed ISS method enables the model to fully learn the labeled information from all datasets. When the threshold of intersection over union (IoU) between the predicted box and ground truth is set to 0.5, the average precision of pedestrians and helmets can reach 0.864 and 0.891, respectively.
  • NETWORKS & SECURITY
    An Improved Equalization with Real Interference Prediction Scheme of the FBMC/OQAM System
    Feng Yang, Yue Wang, Lianghui Ding, Liang Qian
    2021, 18(1): 120-129.
    Abstract ( )   Knowledge map   Save
    FBMC/OQAM transmission system has a better spectral efficiency than OFDM. However, its orthogonality condition is only considered in the real field. In the presence of fading channels, the real orthogonality of FBMC/OQAM might be lost, which calls for new equalization schemes. In this paper, an improved equalizer with real interference prediction (ERIP) scheme of FBMC/OQAM is proposed. We analyze the correlation between the real and the neighboring imaginary interference components taking Doppler shifts into account, and derive the improved ERIP scheme. The simulation results show that the proposed scheme can outperform the original ERIP and the one-tap equalization in time-varying multipath scenarios with an affordable complexity.
  • NETWORKS & SECURITY
    Characteristic Insights on Industrial Cyber Security and Popular Defense Mechanisms
    Ming Wan, Jiawei Li, Ying Liu, Jianming Zhao, Jiushuang Wang
    2021, 18(1): 130-150.
    Abstract ( )   Knowledge map   Save
    Due to the deep integration of information technology and operational technology, networked control systems are experiencing an increasing risk of international cyber attacks. In practice, industrial cyber security is a significant topic because current networked control systems are supporting various critical infrastructures to offer vital utility services. By comparing with traditional IT systems, this paper first analyzes the uncontrollable cyber threats and classified attack characteristics, and elaborates the intrinsic vulnerabilities in current networked control systems and novel security challenges in future Industrial Internet. After that, in order to overcome partial vulnerabilities, this paper presents a few representative security mechanisms which have been successfully applied in today’s industrial control systems, and these mechanisms originally improve traditional IT defense technologies from the perspective of industrial availability. Finally, several popular security viewpoints, adequately covering the needs of industrial network structures and service characteristics, are proposed to combine with burgeoning industrial information technologies. We target to provide some helpful security guidelines for both academia and industry, and hope that our insights can further promote in-depth development of industrial cyber security.
    • SIGNAL PROCESSING
  • SIGNAL PROCESSING
    Cryptanalysis and Improvement of a New Certificateless Signature Scheme in the Standard Model
    Chenhuang Wu, Hui Huang, Kun Zhou, Chunxiang Xu
    2021, 18(1): 151-160.
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    Digital signature, as an important cryptographic primitive, has been widely used in many application scenarios, such as e-commerce, authentication, cloud computing, and so on. Certificateless Public Key Cryptography (PKC) can get rid of the certificate management problem in the traditional Public Key Infrastructure (PKI) and eliminate the key-escrow problem in the identity-based PKC. Lately, a new Certificateless Signature (CLS) scheme has been proposed by Kyung-Ah Shim (IEEE SYSTEMS JOURNAL, 2018, 13(2)), which claimed to achieve provable security in the standard model. Unfortunately, we present a concrete attack to demonstrate that the scheme cannot defend against the Type I adversary. In this type of attack, the adversary can replace the public key of the signer, and then he plays the role of the signer to forge a legal certificateless signature on any message. Furthermore, we give an improved CLS scheme to resist such an attack. In terms of the efficiency and the signature length, the improved CLS is preferable to the original scheme and some recently proposed CLS schemes in the case of precomputation.
  • SIGNAL PROCESSING
    Digital Signature Based on ISRSAC
    Teng Yang, Yanshuo Zhang, Song Xiao, Yimin Zhao
    2021, 18(1): 161-168.
    Abstract ( )   Knowledge map   Save
    Digital signature has recently played an increasingly important role in cyberspace security. Most of them are based on the public key cryptography. Public key cryptography is a mainstream cryptographic algorithm system that has been widely used in cyberspace security in recent years. The most classic public key cryptography algorithm is RSA and its difficulty is based on the large integer decomposition problem. In 2017, ISRSAC was proposed by M.Thangaval. ISRSAC has made security improvements to the RSA algorithm by increasing the complexity in factoring the value of modulus 'n'. A digital signature algorithm based on ISRSAC algorithm was completed in this paper, and furthermore, a proxy signature algorithm based on ISRSAC and two kinds of multi-signature algorithms were presented, which include sequential multi-signature and broadcasting multi-signature.
  • SIGNAL PROCESSING
    Individual Identification of Electronic Equipment Based on Electromagnetic Fingerprint Characteristics
    Han Xu, Hongxin Zhang, Jun Xu, Guangyuan Wang, Yun Nie, Hua Zhang
    2021, 18(1): 169-180.
    Abstract ( )   Knowledge map   Save
    With the rapid development of communication and computer, the individual identification technology of communication equipment has been brought to many application scenarios. The identification of the same type of electronic equipment is of considerable significance, whether it is the identification of friend or foe in military applications, identity determination, radio spectrum management in civil applications, equipment fault diagnosis, and so on. Because of the limited-expression ability of the traditional electromagnetic signal representation methods in the face of complex signals, a new method of individual identification of the same equipment of communication equipment based on deep learning is proposed. The contents of this paper include the following aspects: (1) Considering the shortcomings of deep learning in processing small sample data, this paper provides a universal and robust feature template for signal data. This paper constructs a relatively complete signal template library from multiple perspectives, such as time domain and transform domain features, combined with high-order statistical analysis. Based on the inspiration of the image texture feature, characteristics of amplitude histogram of signal and the signal amplitude co-occurrence matrix (SACM) are proposed in this paper. These signal features can be used as a signal fingerprint template for individual identification. (2) Considering the limitation of the recognition rate of a single classifier, using the integrated classifier has achieved better generalization ability. The final average accuracy of 5 NRF24LE1 modules is up to 98% and solved the problem of individual identification of the same equipment of communication equipment under the condition of the small sample, low signal-to-noise ratio.
  • SIGNAL PROCESSING
    Edge Coloring of Graphs with Applications in Coding Theory
    Ghaffar Raeisi, Mohammad Gholami
    2021, 18(1): 181-195.
    Abstract ( )   Knowledge map   Save
    In this paper, a new type of edge coloring of graphs together with an algorithm for such an edge coloring is presented to construct some column-weight three low-density parity-check (LDPC) codes whose Tanner graphs are free of 4-cycles. This kind of edge coloring is applied on some well-known classes of graphs such as complete graphs and complete bipartite graphs to generate some column-weight 3 LDPC codes having flexibility in terms of code length and rate. Interestingly, the constructed (3,k)-regular codes with regularities k=4,5,…, 22 have lengths n=12, 20, 26, 35, 48, 57, 70, 88, 104, 117, 140, 155, 76, 204, 228, 247, 280, 301, 330, having minimum block length compared to the best known similar codes in the literature. In addition to linear complexity of generating such parity-check matrices, they can be considered as the base matrices of some quasi-cyclic (QC) LDPC codes with maximum achievable girth 18, which inherit the low-complexity encoder implementations of QC-LDPC codes. Simulation results show that the QC-LDPC codes with large girth lifted from the constructed base matrices have good performances and outperform random codes, progressive edge growth LDPC codes, some finite fields and group rings based QC-LDPC codes and also have a close competition to the standard IEEE 802. 16e (WiMAX) code.
    • EMERGING TECHNOLOGLES & APPLICATIONS
  • EMERGING TECHNOLOGLES & APPLICATIONS
    Clustering and Resource Allocation Strategy for D2D Multicast Networks with Machine Learning Approaches
    Fan Jiang, Lan Zhang, Changyin Sun, Zeng Yuan
    2021, 18(1): 196-211.
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    In this paper, the clustering and resource allocation problem in device-to-device (D2D) multicast transmission underlay cellular networks are investigated. For the sake of classifying D2D users into different D2D multicast clusters, a hybrid intelligent clustering strategy (HICS) based on unsupervised machine learning is proposed first. By maximizing the total energy efficiency of D2D multicast clusters, a joint resource allocation scheme is then presented. More specifically, the energy efficiency optimization problem is constructed under the quality of service (QoS) constraints. Since the joint optimization problem is non-convex, we transform the original problem into a mixed-integer programming problem according to the Dinkelbach algorithm. Furthermore, to avoid the high computational complexity inherent in the traditional resource allocation problem, a Q-Learning based joint resource allocation and power control algorithm is proposed. Numerical results reveal that the proposed algorithm achieves better energy efficiency in terms of throughput per energy consumption.
  • EMERGING TECHNOLOGLES & APPLICATIONS
    MimicCloudSim: An Environment for Modeling and Simulation of Mimic Cloud Service
    Liming Pu, Jiangxing Wu, Hailong Ma, Yuhang Zhu, Yingle Li
    2021, 18(1): 212-221.
    Abstract ( )   Knowledge map   Save
    In recent years, an increasing number of application services are deployed in the cloud. However, the cloud platform faces unknown security threats brought by its unknown vulnerabilities and backdoors. Many researchers have studied the Cyber Mimic Defense (CMD) technologies of the cloud services. However, there is a shortage of tools that enable researchers to evaluate their newly proposed cloud service CMD mechanisms, such as scheduling and decision mechanisms. To fill this gap, we propose MimicCloudSim as a mimic cloud service simulation system based on the basic functionalities of CloudSim.MimicCloudSim supports the simulation of dynamic heterogeneous redundancy (DHR) structure which is the core architecture of CMD technology, and provides an extensible interface to help researchers implement new scheduling and decision mechanisms. In this paper, we firstly describes the architecture and implementation of MimicCloudSim, and then discusses the simulation process. Finally, we demonstrate the capabilities of MimicCloudSim by using a decision mechanism. In addition, we tested the performance of MimicCloudSim, the conclusion shows that MimicCloudSim is highly scalable.
  • EMERGING TECHNOLOGLES & APPLICATIONS
    Energy Efficiency Maximization Strategy for Sink Node in SWIPT-Enabled Sensor-Cloud Based on Optimal Stopping Rules
    Zhe Wang, Lina Ge, Taoshen Li, Guifen Zhang, Min Wu
    2021, 18(1): 222-236.
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    Leveraging energy harvesting abilities in wireless network devices has emerged as an effective way to prolong the lifetime of energy constrained systems. The system gains are usually optimized by designing resource allocation algorithm appropriately. However, few works focus on the interaction that channel’s time-vary characters make the energy transfer inefficiently. To address this, we propose a novel system operation sequence for sensor-cloud system where the Sinks provide SWIPT for sensor nodes opportunistically during downlink phase and collect the data transmitted from sensor nodes in uplink phase. Then, the energy-efficiency maximization problem of the Sinks is presented by considering the time costs and energy consumption of channel detection. It is proved that the formulated problem is an optimal stopping process with optimal stopping rules. An optimal energy-efficiency (OEE) algorithm is designed to obtain the optimal stopping rules for SWIPT. Finally, the simulations are performed based on the OEE algorithm compared with the other two strategies to verify the effectiveness and gains in improving the system efficiency.