本期目录

2019年 26卷 6期 刊出日期：2019-12-31



Networks

Performance analysis and enhancement of random access process in cellular-IoT

Xing Shuchen, Wen Xiangming, Lu Zhaoming, Pan Qi, Jing Wenpeng

中国邮电高校学报(英文), 2019, 26 (6). doi： 10.19682/j.cnki.1005-8885.2019.1021

摘要 ( 527 ) PDF (675 KB)( 336 )

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Narrowband Internet of things (NB-IoT) and enhanced machine-type communications (eMTC) are two new IoT-oriented solutions introduced by the 3rd generation partnership project (3GPP) in Rel-13. In order to meet the new requirements (such as long battery life, low device cost, low deployment cost, extended coverage and support for a massive number of devices) of machine-to-machine (M2M) communication, these two technologies had some
improvements on the random access (RA) mechanism compared to traditional long term evolution (LTE). For example, repetition of preamble transmission and coverage enhancement (CE) levels have been proposed to offer communication services in a wider area. In addition, NB-IoT has adopted a new spectrum allocation method and proposed a new type of preamble structure to meet the requirement of big amount of connections. We summarize
details and differences of the RA process in LTE, eMTC and NB-IoT. Afterwards, as an improvement, we propose an enhanced access protocol for NB-IoT. Finally, performance analysis and comparison are presented in terms of access success probability, average access delay, access spectrum efficiency and average number of RA attempts.

Parameters and performance analysis of quantum wireless sensor network for monitoring the activities of tibetan antelope in Hoh Xil

Wei Rongyu, Nie Min, Yang Guang, Zhang Meiling, Sun Aijing, Pei Changx

中国邮电高校学报(英文), 2019, 26 (6). doi： 10.19682/j.cnki.1005-8885.2019.1022

摘要 ( 489 ) PDF (1890 KB)( 267 )

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Hoh Xil is the national nature reserve in China, and Tibetan antelope is a research hotspot of wildlife protection in this area. In order to track the population and activity of Tibetan antelope in Hoh Xil, a quantum wireless sensor monitoring network(QWSMN) based on the quantum satellite wide-area communication networks was proposed. This network consists of quantum wireless sensors installed on the Tibetan antelope, small quantum base stations, quantum satellite signal transmitting stations, quantum satellite and quantum satellite signal receiving stations. The simulation results show that under the interference of the sandstorm, a quantum satellite signal transmitting station can cover the monitoring area of 20 106 km², and the network throughput reaches 40 KB/ s. This network can realize large-scale monitoring of Tibetan antelope in Hoh Xil and provide theoretical basis for the construction of global wildlife monitoring network.

Resource allocation for NOMA based D2D underlaid cellular networks

Chi Linman, Zhu Qi

中国邮电高校学报(英文), 2019, 26 (6). doi： 10.19682/j.cnki.1005-8885.2019.1023

摘要 ( 441 ) PDF (1461 KB)( 231 )

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This paper puts forward a user clustering and power allocation algorithm for non-orthogonal multiple access (NOMA) based device-to-device (D2D) cellular system. Firstly, an optimization problem aimed at maximizing the sum-rate of the system is constructed. Since the optimization problem is a mixed-integer non-convex optimization, it is decomposed into two subproblems, namely user clustering and power allocation subproblem. In the subproblem of user clustering, the clustering algorithms of cellular user and D2D pair are proposed respectively. In the power allocation subproblem, the gradient assisted binary search (GABS) algorithm and logarithmic approximation in successive convex approximation (SCA) are used to optimize the power of subchannel (SC) and D2D transmitted power respectively. Finally, an efficient joint iterative algorithm is proposed for the original mixed inter non-convex non-deterministic polynomial (NP)-hard problem. The simulation results show that the proposed algorithm can effectively improve the total system rate and the larger the ratio of cellular users (CUs) to total users, the larger the total system rate.

Research on adaptive dual task offloading decision algorithm for parking space recommendation service

Peng Weiping, Su Zhe, Song Cheng, Jia Zongpu

中国邮电高校学报(英文), 2019, 26 (6). doi： 10.19682/j.cnki.1005-8885.2019.1024

摘要 ( 466 ) PDF (1125 KB)( 283 )

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In order to improve the efficiency of tasks processing and reduce the energy consumption of new energy vehicle (NEV), an adaptive dual task offloading decision-making scheme for Internet of vehicles is proposed based on information-assisted service of road side units (RSUs) and task offloading theory. Taking the roadside parking space recommendation service as the specific application Scenario, the task offloading model is built and a hierarchical self-organizing network model is constructed, which utilizes the computing power sharing among nodes, RSUs and mobile edge computing (MEC) servers. The task scheduling is performed through the adaptive task offloading decision algorithm, which helps to realize the available parking space recommendation service which is energy-saving and environmental-friendly. Compared with these traditional task offloading decisions, the proposed scheme takes less time and less energy in the whole process of tasks. Simulation results testified the effectiveness of the proposed scheme.

Design and implementation of web-based dynamic geometry drawing technology

Guan Hao, Rao Yongsheng, Xu Zhangtao

中国邮电高校学报(英文), 2019, 26 (6). doi： 10.19682/j.cnki.1005-8885.2019.1025

摘要 ( 544 ) PDF (2106 KB)( 296 )

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Dynamic geometry software, as a piece of computer-assisted instruction (CAI) software, is closely and deeply associated with mathematics, and is widely applied to mathematics teaching activities in primary and secondary schools. Meanwhile, web technology also has become an important technology for assisting education and teaching. This paper expounds a web-based dynamic geometry software development process, and analyses specific requirements regarding graphical application programming interface (API) required by dynamic geometry software. With experiments and comparison on the two different hypertext markup language (HTML) 5 graphical API technologies, i. e. , scalable vector graphics (SVG) and Canvas, on different apparatuses and browsers, we draw the conclusion that it is more suitable to adopt Canvas as the graphical API technology for the web-based dynamic geometry software, thus further proposed the principles and methods for an object-oriented Canvas design. The dynamic geometry software based on the newly-designed Canvas has technical advantages and educational value, well incorporating aesthetic education into mathematics education.

Artificial Intelligence

Improvement of LDA model with time factor for collaborative filtering

Yao Wenbin, Hu Fangyi

中国邮电高校学报(英文), 2019, 26 (6). doi： 10.19682/j.cnki.1005-8885.2019.1026

摘要 ( 538 ) PDF (2002 KB)( 233 )

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Collaborative filtering (CF) is one of the most widely used Algorithm in recommender systems, which help users obtain the information they may like. We proposed a latent Dirichlet allocation (LDA) model combining time and rating (TR-LDA) for CF. We use mathematical methods to fit the Ebbinghaus forgetting curve in our method and introduce time weights based on time window to find out the impact of time on user's interests. The user's choice of items is not only influenced by his/ her interests, but also influenced by other's rating. According to the users' feedback, we find their rating distribution on items under the interests. Finally, experimental results on real data sets MovieLens 100 k and MovieLens 1 M show that the proposed Algorithm can predict the user implicit interests effectively and improve the recommendation performance.

Underflow concentration prediction model of deep-cone thickener based on data-driven

Wang Huan, Liu Ting, Cao Yuning, Wu Aixiang

中国邮电高校学报(英文), 2019, 26 (6). doi： 10.19682/j.cnki.1005-8885.2019.1027

摘要 ( 545 ) PDF (1443 KB)( 247 )

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The underflow concentration prediction of deep-cone thickener is a difficult problem in paste filling. The existing prediction model only determines the influence of some parameters on the underflow concentration, but lacks a prediction model that comprehensively considers the thickening process and various factors. This paper proposed a model which analyzed the variation of the underflow concentration from a number of influencing factors in the
concentrating process. It can accurately predict the underflow concentration. After preprocessing and feature selection of the history data set of the deep-cone thickener, this model uses the eXtreme gradient boosting (XGBOOST) in machine learning to deal with the relationship between the influencing factors and the underflow concentration, so as to achieve a more comprehensive prediction of the underflow concentration of the deep-cone thickener. The experimental results show that the underflow concentration prediction model based on XGBOOST shows a mean absolute error (MAE) of 0.31% and a running time of 1.6 s on the test set constructed in this paper, which fully meet the demand. By comparing the following three classical algorithms: back propagation (BP) neural network, support vector regression (SVR) and linear regression, we further verified the superiority of XGBOOST under the conditions of this study.

Research on virtual reality system design for body and psychological rehabilitation

Liu Tingting, Liu Zhen, Pang Qianchao, Ouyang Menglin, Chai Yanjie

中国邮电高校学报(英文), 2019, 26 (6). doi： 10.19682/j.cnki.1005-8885.2019.1028

摘要 ( 538 ) PDF (4831 KB)( 207 )

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There are many people in China who suffered from physical and mental diseases and need physical and psychological rehabilitation. Traditional treatments can work in rehabilitation, but will take much money and labor. In recent years, virtual reality (VR) technology has become a new direction to innovate rehabilitation. This paper summarizes the research results of VR technology in rehabilitation for stroke, anxiety, depression and autism. Based on these results, we propose a framework for VR rehabilitation system with virtual agents. A prototype system is developed to corroborate the proposed design guidelines. With the prototype rehabilitation system, autistic children can train their life skills in different scenarios. Preliminary test results show the proposed framework can push users to take the rehabilitation training actively and can be a new method for rehabilitation training.

Signal processing

Parallelization of motion compensation algorithm based on reconfigurable video array processor

Xie Xiaoyan, Lei Xiang, Zhou Jinna, Zhu Yun, Jiang Lin

中国邮电高校学报(英文), 2019, 26 (6). doi： 10.19682/j.cnki.1005-8885.2019.1029

摘要 ( 457 ) PDF (4698 KB)( 139 )

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The new encoding tools of high efficiency video coding (HEVC) make the interpolation operation more complex in motion compensation (MC) for better video compression, but impose higher requirements on the computational efficiency and control logic of the hardware architecture. The reconfigurable array processor can take into consideration both the computational efficiency and flexible switching of algorithms very well. Through mining the data dependency and parallelism among interpolation operation, this paper presents a parallelization method based on the dynamic reconfigurable array processor proposed by the project team. The number of pixels loaded from the external memory is reduced significantly, by multiplexing the common data in the previous reference block and the current reference block. Flexible switching of variable block operation is realized by using dynamic reconfiguration mechanism. A 16 x 16 processor element (PE)'s array is used to dynamically process a 4 x 4 - 64 x 64 block size. The experimental results show that, the reference block update speed is increased by 39.9%. In the case of an array size of 16 PEs, the number of pixels processed in parallel reaches 16.

Improved statistical sparse decomposition principle method for underdetermined blind source signal recovery

Wang Chuanchuan, Zeng Yonghu, Wang Liandong, Fu Weihong

中国邮电高校学报(英文), 2019, 26 (6). doi： 10.19682/j.cnki.1005-8885.2019.1030

摘要 ( 416 ) PDF (2513 KB)( 230 )

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Aiming at the statistical sparse decomposition principle (SSDP) method for underdetermined blind source signal recovery with problem of requiring the number of active signals equal to that of the observed signals, which leading to the application bound of SSDP is very finite, an improved SSDP (ISSDP) method is proposed. Based on the principle of recovering the source signals by minimizing the correlation coefficients within a fixed time interval, the selection method of mixing matrix's column vectors used for signal recovery is modified, which enables the choose of mixing matrix's column vectors according to the number of active source signals self-adaptively. By simulation experiments, the proposed method is validated. The proposed method is applicable to the case where the number of active signals is equal to or less than that of observed signals, which is a new way for underdetermined blind source signal recovery.