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  • April 2020 Vol. 17 No. 4
      

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  • Yue Hu, Yafeng Wang, Haocheng Wang
    2020, 17(4): 1-10.
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    Overlapped X domain multiplexing (OvXDM) is a promising encoding technique to obtain high spectral efficiency by utilizing Inter-Symbol Interference (ISI). However, the computational complexity of Maximum Likelihood Sequence Detection (MLSD) increases exponentially with the growth of spectral efficiency in OvXDM, which is unbearable for practical implementations. This paper proposes an OvTDM decoding method based on Recurrent Neural Network (RNN) to realize fast decoding of OvTDM system, which has lower decoding complexity than the traditional fast decoding method. The paper derives the mathematical model of the OvTDM decoder based on RNN and constructs the decoder model. And we compare the performance of the proposed decoding method with the MLSD algorithm and the Fano algorithm. It’s verified that the proposed decoding method exhibits a higher performance than the traditional fast decoding algorithm, especially for the scenarios of a high overlapped multiplexing coefficient.
  • Huifeng Bai, Wenbin Chen, Licheng Wang, Chao Huo
    2020, 17(4): 11-18.
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    As internet services newly emerge with diversity and complexity, great challenges and demands are presented to the OpenFlow controlled software defined optical networks (SDON) to achieve better match between services and SDON. With this aim, this paper proposes a naive Echo-State-Network (Naive-ESN) based services awareness algorithm of the software defined optical network, where the naive ESN model adopts the ring topology structure and generates the probability output result to determine the QoS policy of SDON. Moreover, the Naive-ESN engine is also designed in controller node of SDON to perform services awareness by obtaining service traffic features from data plan, together with some necessary extension of the OpenFlow protocol. Test results show that the proposed approach is able to improved services-oriented supporting ability of SDON.
  • Mohamed Maalej, Hichem Besbes
    2020, 17(4): 19-30.
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    In this paper, we provide analytical results on average Symbol Error Probability (SEP) of a Free Space Optics (FSO) link employing M-ary Pulse Position Modulation (PPM), subjected to turbulent atmosphere modeled with Double Generalized Gamma (DGG) distribution. FSO link is generally impaired by turbulent atmosphere and pointing errors. As far as we know, results are presented in previous works by considering the atmosphere turbulence modeled with a Gamma-Gamma distribution. In our work, we also give asymptotic results on average SEP in order to approximate its evolution at high Signal-to-Noise-Ratio (SNR). Numerical results showed that FSO link performance is enhanced when PPM-modulation index is increased, for both strong and moderate turbulence regimes, and for both strong and weak pointing error jitter. Monte-Carlo simulations were presented to corroborate our analytical expressions.
  • Bin Qi, Jie Ma, Kewei Lv
    2020, 17(4): 31-41.
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    The discrete logarithm problem (DLP) is to find a solution such that in a finite cyclic group , where . The DLP is the security foundation of many cryptosystems, such as RSA. We propose a method to improve Pollard’s kangaroo algorithm, which is the classic algorithm for solving the DLP. In the proposed algorithm, the large integer multiplications are reduced by controlling whether to perform large integer multiplication. To control the process, the tools of expanding factor and jumping distance are introduced. The expanding factor is an indicator used to measure the probability of collision. Large integer multiplication is performed if the value of the expanding factor is greater than the given bound. The improved algorithm requires an average of times of the large integer multiplications. In experiments, the average large integer multiplication times is approximately .
  • Song Ci, Yanglin Zhou, Yuan Xu, Xingjian Diao, Junwei Wang
    2020, 17(4): 42-50.
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    Battery energy storage systems (ESS) have been widely used in mobile base stations (BS) as the main backup power source. Due to the large number of base stations, massive distributed ESSs have largely stayed in idle and very difficult to achieve high asset utilization. In recent years, the fast-paced development of digital energy storage (DES) technology has revolutionized the traditional operation and maintenance of ESSs by transforming them into digital assets, further enabling battery energy storage services, raising up a new way to achieve a much higher utilization of such kind of largely idle ESS resources. In this paper, the disruptive DES technology will be introduced and its application under the context of mobile BSs will be studied, and then a cloud-based energy storage (CES) platform is proposed based on a large scale distributed DESs to provide a new cyber-enabled energy storage service to the local utility company. A real-world case study shows the effectiveness and efficiency of the CES platform.
  • Xiaoyan Li, Xiaohui Zhao, Peng Zhang, Shoufeng Tong
    2020, 17(4): 51-65.
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    Free space optical (FSO) communication system with differential signaling possesses the advantage of requiring no channel state information and avoiding computational load or link throughput reduction compared to the systems with conventional receivers. In this work, we investigate bit error rate (BER) performance of this system over partially and fully correlated atmospheric turbulence fading. In order to conduct the above analysis, we obtain a probability density functions (PDF) of the channel fading on the differential signals and derive our instantaneous BER using differential signaling scheme. Based on these results, we develop two closed-form mathematical expressions for the average BER under fully correlated and partially correlated fading in the convergent infinite series confirmed by Cauchy’s ratio test. The accuracy of the derived BER expressions is demonstrated by the Monte Carlo simulations, and the analyses for the effects of the system parameters on the BER performance are provided.
  • Raad S. Alhumaima
    2020, 17(4): 66-77.
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    The industry of cellular networks is evaluating the new architectures to ensure an enhanced performance. Fog communication is the new paradigm that presented to unleash edge computing. In this paper, we introduced a mathematical framework to evaluate the trade-offs of Fog proposal. Specifically, testing the power consumption, delay and energy efficiency in comparison with traditional cloud radio access networks. Although the literature has showed that fog radio access networks provides an enhanced delay performance, this paper shows that an enlarged amount of power is consumed, which degrades the energy efficiency in comparison with traditional cloud counterpart. However, the level of such devolution depends on the number of deployed fog devices that directly influences the power consumption. This paper also shows that enhancing the delay by using fog architecture is not a straight forward process, but requires a particular caring in terms of choosing the appropriate mode while placing/installing fog functions within fog devices.
  • Jinliang Xu, Shangguang Wang, Ao Zhou, Fangchun Yang
    2020, 17(4): 78-87.
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    Nowadays scalable IoT management is a bottleneck of IoT development due to the geographically dispersed distribution, fragmented ownerships, and ever-growing population of IoT devices. To intelligently manage massive decentralized applications (dApps) in IoT usecases, Edgence (EDGe + intelligENCE) is proposed to use edge clouds to access IoT devices and users, and then use its in-built blockchain to realize self-governing and self-supervision of the edge clouds. Edgence proposes to use masternode technology to introduce IoT devices and users into a closed blockchain system, which can extend the range of blockchain to IoT-based dApps. Further, masternodes do good to scalability by raising the TPS (transactions per second) of the blockchain network. To support various dApps, a three-tier validation is proposed, namely script validation, smartcontract validation, and masternode validation. To avoid energy consumption resulted by blockchain consensus, Edgence proposes a random but verifiable way to elect a masternode to generate each new block. The potential of the tailored Edgence is shown by examples of decentralized crowdsourcing and AI training.
  • Guanghui Yuan, Qi Hao
    2020, 17(4): 88-98.
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    As a means of copyright protection for multimedia data, digital watermarking technology has attracted more and more attention in various research fields. Researchers have begun to explore the feasibility of applying it to remote sensing data recently. Because of the particularity of remote sensing image, higher requirements are put forward for its security and management, especially for the copyright protection, illegal use and authenticity identification of remote sensing image data. Therefore, this paper proposes to use image watermarking technology to achieve comprehensive security protection of remote sensing image data, while the use of cryptography technology increases the applicability and security of watermarking technology. The experimental results show that the scheme of remote sensing image digital watermarking technology has good performance in the imperceptibility and robustness of watermarking.
  • Weijun Zhu, Mingliang Xu
    2020, 17(4): 99-108.
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    Disk Filtration (DF) Malware can attack air-gapped computers. However, none of the existing technique can detect DF attacks. To address this problem, a method for detecting the DF attacks based on the fourteen Machine Learning (ML) algorithms is proposed in this paper. First, we collect a number of data about Power Spectral Density (PSD) and frequency of the sound wave from the Hard Disk Drive (HDD). Second, the corresponding machine learning models are trained respectively using the collected data. Third, the trained ML models are employed to detect whether a DF attack occurs or not respectively, if given pair of values of PSD and frequency are input. The experimental results show that the max accuracy of detection is greater than or equal to 99.4%.
  • Shuifei Zeng, Yan Ma, Xiaoyan Zhang, Xiaofeng Du
    2020, 17(4): 109-124.
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    To achieve good results in convolutional neural networks (CNN) for text classification task, term-based pooling operation in CNNs is proposed. Firstly, the convolution results of several convolution kernels are combined by this method, and then the results after combination are made pooling operation, three sorts of CNN models (we named TB-CNN, MCT-CNN and MMCT-CNN respectively) are constructed and then corresponding algorithmic thought are detailed on this basis. Secondly, relevant experiments and analyses are respectively designed to show the effects of three key parameters (convolution kernel, combination kernel number and word embedding) on three kinds of CNN models and to further demonstrate the effect of the models proposed. The experimental results show that compared with the traditional method of text classification in CNNs, term-based pooling method is addressed that not only the availability of the way is proved, but also the performance shows good superiority.
  • Wenbo Feng, Zheng Hong, Lifa Wu, Menglin Fu, Yihao Li, Peihong Lin
    2020, 17(4): 125-139.
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    How to correctly acquire the appropriate features is a primary problem in network protocol recognition field. Aiming to avoid the trouble of artificially extracting features in traditional methods and improve recognition accuracy, a network protocol recognition method based on Convolutional Neural Network (CNN) is proposed. The method utilizes deep learning technique, and it processes network flows automatically. Firstly, normalization is performed on the intercepted network flows and they are mapped into two-dimensional matrix which will be used as the input of CNN. Then, an improved classification model named PtrCNN is built, which can automatically extract the appropriate features of network protocols. Finally, the classification model is trained to recognize the network protocols. The proposed approach is compared with several machine learning methods. Experimental results show that the tailored CNN can not only improve protocol recognition accuracy but also ensure the fast convergence of classification model and reduce the classification time.
  • Gang Liu, Nan Qi, Jiaxin Chen, Chao Dong, Zanqi Huang
    2020, 17(4): 140-151.
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    Vehicles can establish a collaborative environment cognition through sharing the original or processed sensor data from the vehicular sensors and status map. Clustering in the vehicular ad-hoc network (VANET) is crucial for enhancing the stability of the collaborative environment. In this paper, the problem for clustering is innovatively transformed into a cutting graph problem. A novel clustering algorithm based on the Spectral Clustering algorithm and the improved force-directed algorithm is designed. It takes the average lifetime of all clusters as an optimization goal so that the stability of the entire system can be enhanced. A series of close-to-practical scenarios are generated by the Simulation of Urban Mobility (SUMO). The numerical results indicate that our approach has superior performance in maintaining whole cluster stability.
  • Qi Wu, Yi Cao, Haiming Wang, Wei Hong
    2020, 17(4): 152-164.
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    With the rapid development of modern wireless communications and radar, antennas and arrays are becoming more complex, therein having, e.g., more degrees of design freedom, integration and fabrication constraints and design objectives. While full-wave electromagnetic simulation can be very accurate and therefore essential to the design process, it is also very time consuming, which leads to many challenges for antenna design, optimization and sensitivity analysis (SA). Recently, machine-learning-assisted optimization (MLAO) has been widely introduced to accelerate the design process of antennas and arrays. Machine learning (ML) methods, including Gaussian process regression, support vector machine (SVM) and artificial neural networks (ANNs), have been applied to build surrogate models of antennas to achieve fast response prediction. With the help of these ML methods, various MLAO algorithms have been proposed for different applications. A comprehensive survey of recent advances in ML methods for antenna modeling is first presented. Then, algorithms for ML-assisted antenna design, including optimization and SA, are reviewed. Finally, some challenges facing future MLAO for antenna design are discussed.
  • Yi He, Jianfeng Li, Xiaofei Zhang
    2020, 17(4): 165-179.
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    Effective information fusion is very important in hybrid source localization. In this paper, the performance analysis of conventional joint direction of arrival (DOA) and time difference of arrival (TDOA) system is derived and it is shown that this hybrid system may inferior to the single system when the ratio of angular measurements error to distance measurements error exceeds a threshold. To avoid this problem, an effective DOA/TDOA adaptive cascaded (DTAC) technique is presented. The rotation feature of UAVs and spatial filtering technique are applied to gain the signal-to-noise ratio (SNR), which leads to more accurate estimation of time delay by using DOAs. Nevertheless, the time delay estimation precision is still limited by the sampling frequency, which is constrained by the finite load of UAV. To break through the limitation, an enhanced self-delay-compensation (SDC) method is proposed, which aims at detecting the overlooked time delay within the sampling interval by adding a tiny time delay. Finally, the position of the source is estimated by the Chan algorithm. Compared to DOA-only algorithm, TDOA-only algorithm and joint DOA/TDOA (JDT) algorithm, the proposed method shows better localization accuracy regardless of different SNRs and sampling frequencies. Numerical simulations are presented to validate the effectiveness and robustness of the proposed algorithm.