Table of Content

    29 September 2019, Volume 17 Issue 3
    Special Topic
    Editorial: Special Topic on Data Intelligence in New AI Era
    XU Chengzhong, QIAO Yu
    2019, 17(3):  1-1.  doi:10.12142/ZTECOM.201903001
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    A Lightweight Sentiment Analysis Method
    YU Qingshuang, ZHOU Jie, GONG Wenjuan
    2019, 17(3):  2-8.  doi:10.12142/ZTECOM.201903002
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    The emergence of big data leads to an increasing demand for data processing methods. As the most influential media for Chinese domestic movie ratings, Douban contains a huge amount of data and one can understand users’ perspectives towards these movies by analyzing these data. In this article, we study movie’s critics from the Douban website, perform sentiment analysis on the data obtained by crawling, and visualize the results with a word cloud. We propose a lightweight sentiment analysis method which is free from heavy training and visualize the results in a more conceivable way.

    Big Data-Driven Residents’ Travel Mode Choice: A Research Overview
    ZHAO Juanjuan, XU Chengzhong, MENG Tianhui
    2019, 17(3):  9-14.  doi:10.12142/ZTECOM.201903003
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    The research on residents’ travel mode choice mainly studies how traffic flows are shared by different traffic modes, which is the prerequisite for the government to establish transportation planning and policy. Traditional methods based on survey or small data sources are difficult to accurately describe, explain and verify residents’ travel mode choice behavior. Recently, thanks to upgrades of urban infrastructures, many real-time location-tracking devices become available. These devices generate massive real-time data, which provides new opportunities to analyze and explain resident travel mode choice behavior more accurately and more comprehensively. This paper surveys the current research status of big data-driven residents’ travel mode choice from three aspects: residents’ travel mode identification, acquisition of travel mode influencing factors, and travel mode choice model construction. Finally, the limitations of current research and directions of future research are discussed.

    Face Detection, Alignment, Quality Assessment and Attribute Analysis with Multi-Task Hybrid Convolutional Neural Networks
    GUO Da, ZHENG Qingfang, PENG Xiaojiang, LIU Ming
    2019, 17(3):  15-22.  doi:10.12142/ZTECOM.201903004
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    This paper proposes a universal framework, termed as Multi-Task Hybrid Convolutional Neural Network (MHCNN), for joint face detection, facial landmark detection, facial quality, and facial attribute analysis. MHCNN consists of a high-accuracy single stage detector (SSD) and an efficient tiny convolutional neural network (T-CNN) for joint face detection refinement, alignment and attribute analysis. Though the SSD face detectors achieve promising results, we find that applying a tiny CNN on detections further boosts the detected face scores and bounding boxes. By multi-task training, our T-CNN aims to provide five facial landmarks, facial quality scores, and facial attributes like wearing sunglasses and wearing masks. Since there is no public facial quality data and facial attribute data as we need, we contribute two datasets, namely FaceQ and FaceA, which are collected from the Internet. Experiments show that our MHCNN achieves face detection performance comparable to the state of the art in face detection data set and benchmark (FDDB), and gets reasonable results on AFLW, FaceQ and FaceA.

    RAN Centric Data Collection for New Radio
    GAO Yin, LI Dapeng, HAN Jiren, LIU Zhuang, LIU Yang
    2019, 17(3):  23-30.  doi:10.12142/ZTECOM.201903005
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    Self-organizing network (SON) and minimization of driver tests (MDT) are functions designed for Long Term Evolution (LTE) system. SON is designed for network deployment by automatic configuration. MDT is designed for network performance evaluation by automatic signalling procedure. However, these functions do not support new features in new radio (NR) access technology, e.g., multiple radio access technology (RAT)-dual connectivity (MR-DC), central unit-distribute unit (CU-DU) split architecture, beam, etc. Therefore, how to support these features is a challenge for the industry. This paper provides analysis for these problems and provides the summary of SON/MDT functions progress in 3GPP. The analysis includes sub functions such as inter/intra system mobility robustness enhancement, inter/intra system mobility load balance, measurement qualities and mechanism of MDT, energy saving mechanism and procedure, RACH procedure optimization, PCI selection optimization, coverage and capacity optimization, and quality of service (QoS) monitoring mechanism. In addition, this paper also provides an initial thought on artificial intelligence (AI) algorithms applied to SON/MDT functions in NR, so called Smart Grid.

    Reinforcement Learning from Algorithm Model to Industry Innovation: A Foundation Stone of Future Artificial Intelligence
    DONG Shaokang, CHEN Jiarui, LIU Yong, BAO Tianyi, GAO Yang
    2019, 17(3):  31-41.  doi:10.12142/ZTECOM.201903006
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    Reinforcement learning (RL) algorithm has been introduced for several decades, which becomes a paradigm in sequential decision-making and control. The development of reinforcement learning, especially in recent years, has enabled this algorithm to be applied in many industry fields, such as robotics, medical intelligence, and games. This paper first introduces the history and background of reinforcement learning, and then illustrates the industrial application and open source platforms. After that, the successful applications from AlphaGo to AlphaZero and future reinforcement learning technique are focused on. Finally, the artificial intelligence for complex interaction (e.g., stochastic environment, multiple players, selfish behavior, and distributes optimization) is considered and this paper concludes with the highlight and outlook of future general artificial intelligence.

    Research Paper
    A Low-Cost Outdoor Fingerprinting Localization Scheme For Wireless Cellular Networks
    PEI Dengke, XU Xiaodong, QIN Xiaowei, LIU Dongliang, ZHAO Chunhua
    2019, 17(3):  42-49.  doi:10.12142/ZTECOM.201903007
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    This paper considers outdoor fingerprinting localization in LTE cellular Networks, which can localize non-cooperative user equipment (UEs) that is unwilling to provide Global Positioning System (GPS) information. We propose a low-cost fingerprinting localization scheme that can improve the localization accuracy while reducing the computational complexity. Firstly, a data filtering strategy is employed to filter the fingerprints which are far from the target UE by using the Cell-ID, Timing Advance (TA) and eNodeB environment information, and the distribution of TA difference is analyzed to guide how to use TA rationally in the filtering strategy. Then, improved Weighted K Nearest Neighbors (WKNN) are implemented on the filtered fingerprints to give the final location prediction, and the WKNN is improved by removing the fingerprints that are still far away from the most of the K neighbors. Experiment results show that the performance is improved by the proposed localization scheme, and positioning errors corresponding to Cumulative Distribution Function (CDF) equaling to 67% and 95% are declined to 50 m and 150 m.

    High Speed Polarization-Division Multiplexing Transmissions Based on the Nonlinear Fourier Transform
    WANG Jia, ZHAO Yilong, HUANG Xin, HE Guangqiang
    2019, 17(3):  50-55.  doi:10.12142/ZTECOM.201903008
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    Polarization-division multiplexing (PDM) with modulation in the nonlinear frequency domain consisting of the discrete and/or continuous spectrum has been recently regarded as a useful method to be utilized in optical fiber communication system. It can compensate the optical fiber nonlinearity based on the nonlinear Fourier transform (NFT). In this paper, we combine PDM with the method of nonlinear frequency division multiplexing (NFDM) and demonstrate the achievable transmission rate by increasing the number of multiplexing nonlinear channels. For the selected subcarriers (i.e. 32, 64, and 128), the transmission rates are 64 Gbits/s, 76.8 Gbits/s, and 109.7 Gbits/s respectively by applying 64-quadrature amplitude modulation (64-QAM) on the nonlinear continuous spectrum. For the transmission distance shorter than 1 200 km, the transmission rate of 128-NFDM PDM system can even reach up to 153.6 Gbits/s.

    A Service-Based Intelligent Time-Domain and Spectral-Domain Flow Aggregation in IP-over-EON Based on SDON
    NI Dong, LI Hui, JI Yuefeng, LI Hongbiao, ZHU Yinan
    2019, 17(3):  56-62.  doi:10.12142/ZTECOM.201903009
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    The rapid growth of IP traffic has contributed to wide deployment of optical devices in elastic optical network. However, the passband shape of wavelength selective switches (WSSs) that are used in reconfigurable optical add-drop multiplexer (ROADM)/optical cross connect (OXC) is not ideal, causing the narrowing of spectrum. Spectral narrowing will lead to signal impairment. Therefore, guard-bands need to be inserted between adjacent paths which will cause the waste of resources. In this paper, we propose a service-based intelligent aggregation node selection and area division (ANS-AD) algorithm. For the rationality of the aggregation node selection, the ANS-AD algorithm chooses the aggregation nodes according to historical traffic information based on big data analysis. Then the ANS-AD algorithm divides the topology into areas according to the result of the aggregation node selection. Based on the ANS-AD algorithm, we propose a time-domain and spectral-domain flow aggregation (TS-FA) algorithm. For the purpose of reducing resources’ waste, the TS-FA algorithm attempts to reduce the insertion of guard-bands by time-domain and spectral-domain flow aggregation. Moreover, we design a time-domain and spectral-domain flow aggregation module on software defined optical network (SDON) architecture. Finally, a simulation is designed to evaluate the performance of the proposed algorithms and the results show that our proposed algorithms can effectively reduce the resource waste.

    Data-Driven Joint Estimation for Blind Signal Based on GA-PSO Algorithm
    LIU Shen, QIN Yuannian, LI Xiaofan, ZHAO Yubin, XU Chengzhong
    2019, 17(3):  63-70.  doi:10.12142/ZTECOM.201903010
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    Without any prior information about related wireless transmitting nodes, joint estimation of the position and power of a blind signal combined with multiple co-frequency radio waves is a challenging task. Measuring the signal related data based on a group distributed sensor is an efficient way to infer the various characteristics of the signal sources. In this paper, we propose a particle swarm optimization to estimate multiple co-frequency “blind” source nodes, which is based on the received power data measured by the sensors. To distract the mix signals precisely, a genetic algorithm is applied, and it further improves the estimation performance of the system. The simulation results show the efficiency of the proposed algorithm.

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    The whole issue of ZTE Communications September 2019, Vol. 17 No.3
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