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    20 December 2022, Volume 38 Issue 12
    Research and Development
    Virtual machine scheduling model based on Shapley value modified genetic algorithm in cloud computing
    Kun MA, Lingyu XU, Xiaoping SHEN, Zhicheng GONG, Jianping LAN, Shuangxi CHEN, Jun QIAN
    2022, 38(12):  1-10.  doi:10.11959/j.issn.1000-0801.2022281
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    Cloud computing system has the characteristics of large-scale servers and a wide range of users.However, it also consumes a huge number of energy, resulting in high operating costs of cloud providers and high carbon emissions issue.Cloud computing is highly virtualized.How to allocate and manage the virtual resources to ensure efficient physical resource utilization and energy consumption control is a multi-parameter game problem, and it is also a research hotspot in this field.A virtual machine scheduling model and the corresponding SV-GA were proposed, which could calculate the contribution value of the physical machine participating in the work through the Shapley value, and modify the probability parameter of the mutation step in the genetic algorithm through the contribution value, so as to complete the task of virtual machine scheduling.The experimental results show that during the comparison with Max-Min, LrMmt and DE, the SV-GA shows its excellent performance in the multi-parameter game including migration time, times, SLA violation rate and energy consumption in the virtual machine scheduling process.

    Variable granularity service function chain mapping algorithm based on microservice architecture
    Xiaochun WU, Chen HONG, Yue ZHANG, Junnan ZHANG, Jingjing ZHOU
    2022, 38(12):  11-26.  doi:10.11959/j.issn.1000-0801.2022289
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    For the problem that the end-to-end delay of the service function chain (SFC) cannot meet the demand of delay-sensitive applications in 5G environment, a variable granularity service function chain mapping (VG-SFCM) algorithm based on microservice architecture was proposed by splitting the traditional virtualized network function (VNF) into mapping units of varying granularity.Firstly, the traditional coarse-grained VNF was decoupled into fine-grained microservice units, and then the instantiation of microservice units was reduced through the consolidation of redundant microservice units within SFC and the reuse of microservice units between SFC, thus reducing the processing time of SFC.The simulation results show that the algorithm reduces the end-to-end delay of SFC by 14.81% compared to the traditional mapping algorithm while reducing the average deployment network cost.

    Study on the direction of artificial intelligence technology based on network evolution
    Zhen YANG, Jianjun ZHAO, Yongjun HUANG, Jie LI, Nan CHEN
    2022, 38(12):  27-34.  doi:10.11959/j.issn.1000-0801.2022298
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    The national strategy positions artificial intelligence (AI) and Internet of things (IoT)/5G as important components of information technology facilities at the same time.Among them, AI belongs to new technology infrastructure, and IoT/5G belongs to communication network infrastructure.It leads to the technical direction of “integrate and develop communication and AI technology”.For telecom operators, how to integrate AI technology with the network, reconstruct network technology architecture, and open “AI ability” as a “service” will be an important technology evolution direction.Based on this proposition, the development direction of AI and network technology in the future was discussed, and a reference idea for the integration development and application direction of AI technology with the network architecture of IoT/5G in the future was provided.

    Statistical modeling based fast rate estimation algorithm for VVC
    Wei QI, Haibing YIN, Hongkui WANG, Xiaofeng HUANG, Weihong NIU
    2022, 38(12):  35-45.  doi:10.11959/j.issn.1000-0801.2022279
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    To reduce the coding complexity of the rate-distortion optimization process of the latest video coding standard versatile video coding (VVC), a fast rate estimation model based on statistical modeling was proposed.Firstly, the quantization behavior in dependent quantization (DQ) and the context dependency in entropy coding were fully considered.Features that could describe context state transition in the coding process were proposed to estimate rate of some synatax elements in a TU preliminarily.Secondly, coefficient chaos and sparsity features were proposed to distinguish the influence of coefficient distribution difference on the rate cost based on the coefficient distribution characteristics which built a TU level rate model.Finally, large-size transform unit (TU) and small-size TU was modeling respectively according to the rate composition character to achieve more accurate rate estimation.A large number of parameters were trained by regression model through statistical methods, and the final linear rate model was obtained which was applied to the mode decision.Experimental results show that the proposed algorithm can achieve 16.289% complexity reduction with 1.567% BD-BR increase for RA configuration.

    A genetic algorithm based method of optimizing dispersion matrix for RDSM system
    Peng ZHANG, Xiaoping JIN, Dongxiao CHEN
    2022, 38(12):  46-55.  doi:10.11959/j.issn.1000-0801.2022291
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    Rectangular differential spatial modulation (RDSM) is a multi-antenna incoherent modulation technology with high spectral efficiency, low power consumption, and zero-overhead for channel estimation.RDSM is especially suitable for 6G communication systems, such as fast-moving Internet of vehicles, Internet of things, cellular networks, etc.However, the construction of the sparse rectangular unitary space-time dispersion matrix (DM) at transmitter is a problem.The proposed Genetic algorithm (GA) will result in less computational complexity than the currently used random research.The fitness of GA was calculated by the rank and determinant criterion (RDC) method to avoid discussions in differential system.Due to characteristics of constellation symbols of RDSM, the proposed method reduced the computational complexity during each single iteration in GA.The simulation results show that the optimized DMS can significantly improve the bit error rate (BER) performance of the RDSM system.Compared with random search, the low-complexity GA effectively improves the DMS optimization efficiency of RDSM.The computational complexity required for optimizing DMS is about 0.1% of random search optimization method.

    Deep learning Chinese input method with incremental vocabulary selection
    Huajian REN, Xiulan HAO, Wenjing XU
    2022, 38(12):  56-64.  doi:10.11959/j.issn.1000-0801.2022294
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    The core task of an input method is to convert the keystroke sequences typed by users into Chinese character sequences.Input methods applying deep learning methods have advantages in learning long-range dependencies and solving data sparsity problems.However, the existing methods still have two shortcomings: the separation structure of pinyin slicing in conversion leads to error propagation, and the model is complicated to meet the demand for real-time performance of the input method.A deep-learning input method model incorporating incremental word selection methods was proposed to address these shortcomings.Various softmax optimization methods were compared.Experiments on People’s Daily data and Chinese Wikipedia data show that the model improves the conversion accuracy by 15% compared with the current state-of-the-art model, and the incremental vocabulary selection method makes the model 130 times faster without losing conversion accuracy.

    Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior
    Huahua CHEN, Zhe CHEN
    2022, 38(12):  65-77.  doi:10.11959/j.issn.1000-0801.2022238
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    Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.

    Task security scheduling method for 5G+MEC based grid edge computing platform
    Shimulin XIE, Ijie BA, Xiang ZHANG, Zeyi TANG, Weifan NIAN, Xujie LIU
    2022, 38(12):  78-85.  doi:10.11959/j.issn.1000-0801.2022262
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    In order to ensure the security of task scheduling of grid edge computing platform and the data quality required by task scheduling, a task security scheduling method of grid edge computing platform based on 5G + MEC was proposed.Combined with confidentiality service and integrity service, the security level model of task scheduling was constructed to restrict the risk in the process of scheduling and transmission of scheduling task queue, so as to realize the secure transmission of 5G core network.The priority queue type was confirmd, the minimum queue and the maximum queue was selected, the maximization of data resources and the task scheduling of MEC equipment was supported, and a distributed task scheduling model was built.Using Lyapunov candidate function to improve the stability of task scheduling, and the model was solved by alternating direction multiplier method to obtain the optimal solution of task security scheduling.The test results show that after the application of this method, the risk probability results fluctuate in the range of 0.15~0.35, and the fitting degree between the relevant data provided by MEC equipment and the scheduling task of core server is higher than 0.92, the quality score of task scheduling data is also higher than 0.94.

    Automatic control technology of low frequency and low voltage splitting devices using wide area measurement information
    Mingliang GAO, Bo ZHUANG, Xuefei NIU, Mouying LI
    2022, 38(12):  86-93.  doi:10.11959/j.issn.1000-0801.2022297
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    Aiming at the problems of large noise content of wide area measurement information of power grid and difficulty in controlling oscillation damping in interconnected areas of power grid, an automatic control technology of low frequency and low voltage splitting devices using wide area measurement information was proposed.Based on the wide area measurement system (WAMS), the wide area measurement information of the power grid during the operation was obtained.After the noise contained in the wide area measurement information of the power grid was removed by the wavelet decomposition de-noising method, the power grid low-frequency oscillation identification method based on Prony was used to identify the power grid oscillation region and oscillation value.Then, based on the grid oscillation region and oscillation value, the oscillation center voltage and positioning coefficient were calculated and used as input to design the automatic control flow of the low frequency and low voltage splitting device, so as to realize the automatic control of the low frequency and low voltage splitting device.The experimental results show that this method can effectively remove the noise contained in the wide area measurement information of the power grid, the maximum error of the oscillation damping value in the interconnected area of the power grid is only 0.01, and this method can effectively control the low frequency and low voltage splitting device.

    A new method to improve 5G coverage of urban grid roads
    Yejiang ZHANG, Jie CHEN, Jian HU, Xiaokang YANG, Yun LU, Teng LIANG
    2022, 38(12):  94-102.  doi:10.11959/j.issn.1000-0801.2022299
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    Urban grid road is an important use scenario for mobile communication users.Compared with other static scenes, UE mobility on the road leads to relatively poor customer perception.The traditional urban grid road test optimization has some pain points, such as incomplete regulation, low optimization efficiency, repeated work orders, no focus and so on.A new method to improve 5G coverage of urban grid roads was proposed.The proposed method introduces the concept of problem circle, realizes the effective aggregation of sporadic test problem points, and uses“aggregation degree” and “repetition degree” to quantitatively identify the priority of test problems.By introducing the concepts of “ideal azimuth” and “appropriate cell”, the method of the optimal coverage cell of the problem circle was proposed, which can effectively output the optimization scheme of the problem circle.This method has been deployed in the current network and effectively supports the test and optimization of urban grid roads.The 5G coverage rate has increased from 89.95% to 98.86%.

    A big data framework for short-term power load forecasting using heterogenous data
    Haibo ZHAO, Zhijun XIANG, Linsong XIAO
    2022, 38(12):  103-111.  doi:10.11959/j.issn.1000-0801.2022292
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    The power system is in a transition towards a more intelligent, flexible and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in future grid planning and operation.A big data framework for short-term power load forcasting using heterogenous was proposed, which collected the data from smart meters and weather forecast, pre-processed and loaded it into a NoSQL database that was capable to store and further processing large volumes of heterogeneous data.Then, a long short-term memory (LSTM) recurrent neural network was designed and implemented to determine the load profiles and forecast the electricity consumption for the residential community for the next 24 hours.The proposed framework was tested with a publicly available smart meter dataset of a residential community, of which LSTM’s performance was compared with two benchmark algorithms in terms of root mean square error and mean absolute percentage error, and its validity has been verified.

    Engineering and Application
    Research on co-spectrum sharing technology in LTE 2.1 GHz
    Bei LI, Yuhua HU, Guanghai LIU, Tian XIAO, Chuntao SONG, Yi ZHANG, Bo WANG, Qunqing WANG
    2022, 38(12):  112-120.  doi:10.11959/j.issn.1000-0801.2022251
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    Spectrum sharing technology in 5G was introduced in the 3GPP standard.The types and principles of spectrum sharing were presented, the key factors for the realization of dynamic co-spectrum sharing were analyzed, the application configuration and suggestion for the implementation of dynamic co-spectrum sharing were presented, the effect of network application was verified, and the limitations of dynamic co-spectrum sharing in the existing network were summarized.

    Research and application exploration of threat intelligence system of telecom operators
    Haitao ZHANG, Yi JIANG, Shijie ZHU, Qi CHEN
    2022, 38(12):  121-132.  doi:10.11959/j.issn.1000-0801.2022293
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    With the increasing inequality of network attack and defense in the Internet era, threat intelligence has become one of the important tools to narrow this gap.Based on the analysis of the research status of threat intelligence at home and abroad, a set of construction methods of threat intelligence systems suitable for telecom operators were proposed, including six steps: intelligence planning, intelligence production, intelligence analysis, intelligence management, intelligence sharing and intelligence application.Meanwhile a set of multi-source intelligence fusion assessment mechanisms was presented, and the technologies and methods were systematically expounded involved in the four stages of intelligence aggregation analysis, intelligence reputation analysis, intelligence correlation analysis and intelligence aging analysis, so as to help the telecom operators build the ability of intelligence fusion analysis.At the same time, the principles of intelligence production and synchronous application were given for intrusion and loss intelligence, which provided a useful reference for telecom operators to apply threat intelligence technology to build a security protection system.

    Research on SRv6 header compression schemes
    Xia GONG, Huanan CHEN, Yongqing ZHU, Youming WU, Ke RUAN
    2022, 38(12):  133-140.  doi:10.11959/j.issn.1000-0801.2022282
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    Digital and intelligent carrier network have become the priorities of network construction and operation.As a key technology to realize network interconnection and service carrying, carrier protocol critically decides the network scale and traffic efficiency.Facing the service consistent experience requirements, SRv6 as the most competitive technology of IPv6 evolution, has become the development direction in the future.Combined with the principle of SRv6 technology, the requirements of SRv6 compression were analyzed.Based on the main SRv6 compression schemes in the industry, the advantages and disadvantages of each scheme were analyzed, so as to provide guidance for the large scale deployment of SRv6 technology.

    Research and verification of cement smart factory solution based on 5G MEC
    Bo GAO, Ke MA, Jianmin ZHANG, Guirong DENG, Zhenhan ZANG
    2022, 38(12):  141-151.  doi:10.11959/j.issn.1000-0801.2022296
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    With the continuous advancement of the new industrialization process, how 5G can empower industrial digital transformation and help enterprises achieve industrial upgrading has received more and more attention.According to the cement production process, the demand of the industry was analyzed, a cement smart factory solution based on 5G multi-access edge computing (MEC) was proposed, and typical 5G application scenarios were given.Combined with actual commercial cases, the detailed deployment networking and security protection solutions were introduced, and the significant effect of the solution in helping enterprises to reduce costs and increase efficiency was verified.It provided a reference for the 5G MEC scenario application in the cement building materials industry and even the process industry.

    Research on technology mechanism used in P2P network video surveillance system
    Xinyan FANG, Yanxia ZHANG, Ruilin CUI, Siwei LUO
    2022, 38(12):  152-162.  doi:10.11959/j.issn.1000-0801.2022295
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    At present, the scale of video monitoring of small and medium-sized enterprises and families in the civil market is growing explosively.In view of the monitoring demand of tens of millions of cameras, peer to peer (P2P) network video monitoring technology was widely used in the industry.The principle of P2P network video monitoring technology was expounded comprehensively, and the streaming media control technology, transmission technology and private network crossing technology adopted by the mainstream P2P network video monitoring were analyzed.Finally, a large-scale deployment of P2P video surveillance solution was proposed.

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Authorized by: China Association for Science and Technology
Sponsored by: China Institute of Communications
Posts and Telecom Press Co., Ltd.
Publisher: Beijing Xintong Media Co., Ltd.
Editor-in-Chief: Chen Shanzhi
Editorial Director: Li Caishan
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