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    20 November 2019, Volume 35 Issue 11
    research and development
    Resource allocation algorithm for the downlink of multi-carrier and multi-user NOMA systems
    Jie YAN,Rongfang SONG
    2019, 35(11):  1-8.  doi:10.11959/j.issn.1000-0801.2019230
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    Non-orthogonal multiple access (NOMA) technology had received considerable attention as a candidate technology for 5G cellular systems.The resource allocation problem in multi-carrier and multi-user downlink NOMA systems were studied with the goal of optimizing system throughput.In the solution of the problem,the linear water-filling power allocation was used between subcarriers as the fractional transmit power allocation algorithm was used between the superposed users,so that system throughput could be improved.Meanwhile,the concept of time slot was adopted in the design of NOMA transmission scheme,in order to be able to schedule more users in the scenario where the number of cellcenter users and celledge users were not equal.The whole time period was divided into two time slots.On the basis of ensuring the fairness of users,different user pairing groups were implemented in different time slots so as to fully utilizing the entire resource to improve system throughput.The simulation results show that this method can transmit more bits under the same transmit power comparing with traditional non-orthogonal multiple access(NOMA) and OFDMA.

    Structural properties and static robustness of Linux software network
    Ershen WANG, Yu LI, Chen HONG, Qinghua SUN, Chang LIU
    2019, 35(11):  9-18.  doi:10.11959/j.issn.1000-0801.2019273
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    The source code of the Linux software was analyzed,and the directed function call network model,the weak coupling network model of software system,the strong coupling network model and the strong connected subgraph model were built.The characteristics and static robustness of the four networks were studied.The results show that four networks all show ‘small-world’ property.The out-degree distribution of the six modules in the Linux network generally follows the exponential distribution,and the in-degree distribution of the six modules follows the power law.The best attacking strategy was distinct when different robustness evaluation indicator was adopted.For the weak connectivity indicator,high in-degree attacking strategy was of the greatest damage to the weakly coupled software network.

    research and development
    An efficient spectrum sensing of mixed kernel SVM based on sampling covariance matrix
    Jianyuan NIE,Jianrong BAO,Bin JIANG,Chao LIU,Fang ZHU,Jianhai HE
    2019, 35(11):  19-26.  doi:10.11959/j.issn.1000-0801.2019210
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    In recent years,with the blind detection algorithms were proposed,more and more blind algorithms based on sampling covariance matrix were applied to spectrum sensing.The detection threshold was an approximation,and the detection performance would be affected for this algorithms.Thus,the mixed kernel function support vector machine (SVM) efficient spectrum sensing based on sampling covariance matrix was proposed.The statistics which were maximum minimum eigenvalue (MME) and covariance absolute value (CAV) of sensing signal sampling covariance matrices were used as the feature vectors of SVM and were trained to generate a spectrum sensing classifier.The advantage of this algorithm was that it needn’t calculate the detection threshold and the extraction of features reduces size of the sample set.The genetic algorithm (GA) was used to optimize the parameters of mixed kernel function SVM algorithm.The experimental results show that the proposed method has higher detection probability than MME and CAV algorithms,and has less sensing time than SVM,which has good practicability.

    AdaBoost algorithm based on fitted weak classifier
    Pengfeng SONG,Qingwei YE,Zhihua LU,Yu ZHOU
    2019, 35(11):  27-35.  doi:10.11959/j.issn.1000-0801.2019219
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    AdaBoost algorithm was proposed to minimize the accuracy caused by weak classifiers by minimizing the training error rate,and the single threshold was weaker and difficult to converge.The AdaBoost algorithm based on the fitted weak classifier was proposed.Firstly,the mapping relationship between eigenvalues and marker values was established.The least squares method was introduced to solve the fitting polynomial function,and the continuous fitting values were converted into discrete categorical values,thereby obtaining a weak classifier.From the many classifiers obtained,the classifier with smaller fitting error was selected as the weak classifier to form a new AdaBoost strong classifier.The UCI dataset and the MIT face image database were selected for experimental verification.Compared with the traditional Discrete-AdaBoost algorithm,the training speed of the improved algorithm was increased by an order of magnitude.And the face detection rate can reach 96.59%.

    Multi-constrained trusted cooperative task migration strategy for edge computing
    Guangxue YUE,Yasheng DAI,Xiaohui YANG,Youkang ZHU,Zhenxu YOU,Jiansheng LIU
    2019, 35(11):  36-50.  doi:10.11959/j.issn.1000-0801.2019275
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    In order to guarantee the quality of service of edge computing,a trusted cooperative task distribution strategy for edge computing under multi-dimensional constraints was proposed.The strategy was based on the task requirements,and the edge computing collaborative service leader node organizes the coordinated service alliance members.The priority of cooperative service alliance was determined based on the K-dimension weight index of user task migration.Using the load balancing of the members as the adaptive function,the task allocation and dispatch of alliance members were performed by greedy algorithm.The backup node was selected based on routing piggyback,and the priority of migration was evaluated.The scheduling and migration of collaborative services in case of abnormal collaborative services were realized,which improved the quality of service of edge computing task migration and ensured the reliability of task migration.The simulation results show that the mechanism can effectively complete the task distribution and migration scheduling,improve the collaborative efficiency of edge computing,and guarantee the quality of network service.

    Newton iteration and SOR iteration based WWSE precoding algorithm in massive MIMO systems
    Wensheng SUN,Junjie XU
    2019, 35(11):  51-57.  doi:10.11959/j.issn.1000-0801.2019278
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    In massive MIMO system,Newton iteration algorithm was applied to the traditional minimum mean-square error (WWSE) precoding algorithm to calculate the inverse operation,but the initial value of iteration was complicated.Aiming at this problem,WWSESOR-NT algorithm was proposed.The intermediate algorithm based on the SOR iterative algorithm was proposed.Then combined with Newton iteration algorithm,the intermediate algorithm was used to directly estimate the inverse of higher-order matrix,and the result was taken as the initial value of Newton iteration algorithm to accelerate the convergence speed.The simulation results show that WWSESOR-NT algorithm can approximate the performance of WWSE algorithm with fewer iterations and approximately the same complexity.

    Review of image classification based on deep learning
    Fu SU,Qin LV,Renze LUO
    2019, 35(11):  58-74.  doi:10.11959/j.issn.1000-0801.2019268
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    In recent years,deep learning performed superior in the field of computer vision to traditional machine learning technology.Indeed,image classification issue drew great attention as a prominent research topic.For traditional image classification method,huge volume of image data was of difficulty to process and the requirements for the operation accuracy and speed of image classification could not be met.However,deep learning-based image classification method broke through the bottleneck and became the mainstream method to finish these classification tasks.The research significance and current development status of image classification was introduced in detail.Also,besides the structure,advantages and limitations of the convolutional neural networks,the most important deep learning methods,such as auto-encoders,deep belief networks and deep Boltzmann machines image classification were concretely analyzed.Furthermore,the differences and performance on common datasets of these methods were compared and analyzed.In the end,the shortcomings of deep learning methods in the field of image classification and the possible future research directions were discussed.

    Operation technology wide Angle
    New multicast technology (BIER) and prototype system
    Jinghai YU,Zheng ZHANG,Shaofu PENG
    2019, 35(11):  75-83.  doi:10.11959/j.issn.1000-0801.2019221
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    The principle and advantages of BIER were introduced,and it was compared with traditional multicast technology.It was pointed out that BIER technology was an important choice for future network evolution.At the same time,through the introduction of the prototype system,the feasibility of the BIER and the evolution capability were analyzed in detail.It was predicted that BIER technology was an inevitable choice of controllable multicast under the trend of SDN evolution.

    Measures and optimization methods of communications network energy efficiency
    Xiuying NIE
    2019, 35(11):  84-87.  doi:10.11959/j.issn.1000-0801.2019276
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    Although network played an important role in energy efficiency improvement for many industries,network energy consumption had threatened the sustainable development of network operators,due to the fast increasing of the networks’ energy consumption recent years as the swift development of internet users,internet services and applications.The energy consumption risk faced by network operators,the research development of the measures and optimization methods of network energy efficiency was introduced.The energy efficiency optimization in the future network was finally concluded.

    Wireless communication network optimization scheme for high-speed railway based on MEC
    Bin LI
    2019, 35(11):  88-95.  doi:10.11959/j.issn.1000-0801.2019199
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    The optimization scheme of high-speed railway wireless communication network based on MEC was proposed,in order to achieve the optimization of high-speed railway wireless communication network and meet the needs of safe,reliable and efficient multi-dimensional railway transportation of high-speed railway,based on the analysis of existing high-speed railway vehicle-ground wireless communication network,and combining with MEC which was widely used and popularized in other industries,.Comparing the two models of wireless communication network optimization scheme of high-speed railway,the function of MEC server and MEC platform structure of base station were expounded.Through the application example of high-speed railway,the optimization scheme of wireless communication network based on MEC was systematically described.It provided theoretical basis for the future optimization of wireless communication network of high-speed railway,and provided model support for the research on network delay,wireless transmission optimization,virtualization technology and other aspects.

    Integration development trend of the internet of vehicles industry
    Libo TANG,Chen KANG
    2019, 35(11):  96-100.  doi:10.11959/j.issn.1000-0801.2019277
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    Starting from communication technology evolution,the ability of communication technology to support automatic driving was studied.The upgrade trend of internet of vehicles services and the domestic and international policies were proposed.A major breakthrough in China’s internet of vehicles policy and industry was analyzed,with a good development environment.The integration and accelerated development trend of the internet of vehicles industry was concluded.

    Accelerated algorithm in attribute-based encryption over cloud environment
    Guowei WU,Ning FAN,Laifu WANG,Shuai WANG,Jun SHEN,Huamin JIN
    2019, 35(11):  101-107.  doi:10.11959/j.issn.1000-0801.2019145
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    Cloud computing provides storage,computing and network services to tenants.Data security and data sharing and access control between tenants are indispensable functions.Attribute-based encryption (ABE) system is a one to many encryption system,which allows users to fine -grained access control on encrypted data according to users’ attributes.This encryption system is very suitable for multi-tenant data sharing overcloud computing environment.However,the existing attribute-based encryption schemes is inefficient,which cannot be applied to practical applications.Two types of ABE and their application scenarios were analyzed,and an acceleration algorithms scheme in attribute encryption cryptosystem was proposed.The experiment can illustrate that the proposed scheme can improve the efficiency of key generation algorithm,encryption algorithm and decryption algorithm.

    Power informatization column
    Power services scheduling algorithm based on quantum communication
    Zhuozhi YU,Yefeng ZHANG,Bingyang HAN,Wenwei CHEN,Dong WANG
    2019, 35(11):  108-116.  doi:10.11959/j.issn.1000-0801.2019279
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    Quantum communication could effectively improve the transmission’s reliability and security of power system services.However,limited by the generating rate of quantum key,it was difficult to meet the encryption requirements of important services.Therefore,a queue scheduling algorithm was required to legitimately schedule the power system services which need to be encrypted in quantum communication.An improved weighted fair queuing (weighted fair queuing,WFQ) algorithm LD-WFQ was proposed.By estimating the expected delay of the data packets to be encrypted,sending the packets that would soon be timeout,the proposed algorithm effectively reduced the timeout ratio of the packets and improved the effective utilization of quantum keys,while maintaining the timeout ratio of the packets in high priority queue at a low level.Compared with the WFQ algorithm,the simulation result proves the superiority of LD-WFQ algorithm.

    Application of big data method in forecasting potential sensitive customers of electric power
    Xiaofeng CHEN,Yadi ZHAO,Lipeng ZHANG,Feng ZHU
    2019, 35(11):  117-124.  doi:10.11959/j.issn.1000-0801.2019271
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    With the continuous development and extension of 95598 business,the intensity of manual telephone traffic increases.In order to further deepen the consciousness and understanding of the hidden features and the demands of the customers,improve the customer service level of 95598,the typical application scenarios in the customer service such as the tendency of the complaint were refined.Based on the data of power service orders,the key index of modeling was selected.Through entropy weight method,principal component analysis and decision tree and other data mining algorithms,potential complaint propensity customers and planned blackout sensitive customers in order to carry out targeted service resource scheduling were identified,fully do a good job of response measures,effectively reduce complaint pressure and improve service accuracy.

    An analysis method for electricity consumption in peak load period of summer based on regression analysis
    Yuze CHEN,Lei LI,Xuemin FANG,Jian LIU,Jiakui ZHAO,Yuxi LIU,Songlin ZUO
    2019, 35(11):  125-131.  doi:10.11959/j.issn.1000-0801.2019274
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    A method for analyzing the relationship between daily maximum temperature and electricity consumption during peak load period was proposed.Firstly,the relationship between maximum temperature and electricity consumption was quantified by Pearson correlation coefficient.Secondly,the functional relationship was established using regression and solved by least squares method.The quantified result of the relationship between maximum temperature and electricity consumption was then acquired.The empirical research shows the proposed method for electricity consumption analysis during peak load period of summer can quantify the variation of daily electricity consumption caused by temperature change and predict electricity consumption of the next few days according to the analysis result.

    Knowledge graph based ubiquitous power IoT security visualization technology
    Yang YU,Shaomin ZHU,Chaoyi BIAN
    2019, 35(11):  132-139.  doi:10.11959/j.issn.1000-0801.2019280
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    With the construction of the ubiquitous power IoT,the power network transform to interconnection,the data become more interactive and shared,and the business transform to lateral link-up,which pose new challenges to the original network security protection system.Network security visualization technology helps network security personnel quickly identify potential attacks,locate abnormal events,discover new types of attacks,and quickly capture global network security situation by displaying and analyzing the graph patterns.Based on the knowledge graph,the ubiquitous power IoT security analysis was carried out.The internal and external threat intelligence was modeled firstly.Then the relationship between the conceptual entities was constructed.The threat intelligence as knowledge graph was modeled,and then the abstract and complex alarm information which was invisible was converted into a more intuitive and convenient style,which provided accurate support for ubiquitous network security protection decisions in the power IoT.

    Research and practice on power big data security governance system
    Hongbin ZHU,Long AN,Mingchen YANG
    2019, 35(11):  140-145.  doi:10.11959/j.issn.1000-0801.2019281
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    The blackmail attacks and data leakage problems of big data had become increasingly serious in recent years,and the state had successively issued a series of relevant laws and regulations to clarify the requirements for big data security work.Power big data assets had the characteristics of large data volume,wide distribution,high utilization value and high sensitivity.It was urgent to carry out research on big data security governance technology.Based on the analysis of the challenges faced by power big data security,the power big data security governance system was designed,the power big data security governance requirements were put forward,and the data security management and control was verified by the research and development of power big data security governance platform.

    Architecture and key technologies of the operation support system for intensive monitoring of provincial and local power communication network
    Zhimin YANG,Danke HONG,Yu HUANG
    2019, 35(11):  146-156.  doi:10.11959/j.issn.1000-0801.2019269
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    In order to support the intensive monitoring of provincial and local power communication network,the existing operation support system was optimized and upgraded.A system architecture which takes the communication dispatcher as information center was presented,and several key technologies of monitoring,control and resource scheduling were developed.The achievements were widely used in the power grid companies of five provinces in southern China.The results show that both the operation efficiency and the level of safe operation of the intensive monitoring for provincial-local power communication network has been significantly improved,which provides more reliable and higher quality communication support for both the safe and stable operation of the power grid and the digital transformation of the company.

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