- Zheng Wang;Chaokun Wang;Xiaojun Ye;Jisheng Pei;Bin Li;
Information diffusion is one of the most important issues in social network analysis.Unlike most existing works,which either rely on network topology or node profiles,this study focuses on the diffusion itself,i.e.,the recorded propagation histories.These histories are the evidence of diffusion and can be used to explain to users what happened in their networks.However,these histories can quickly grow in size and complexity,limiting their capacity to be intuitively understood.To reduce this information overload,in this paper we present the problem of propagation history ranking.The goal is to rank participant edges/nodes by their contribution to the diffusion.We first discuss and adapt a causal measure,Difference of Causal Effects(DCE),as the ranking criterion.Then,to avoid the complex calculation of DCE,we propose two integrated ranking strategies by adopting two indicators.One is responsibility,which captures the necessity aspect of causal effects.We further give an approximate algorithm,which could guarantee a feasible solution,for this indicator.The other is capability,which captures the sufficiency aspect of causal effects.Finally,promising experimental results are presented to verify the feasibility of the proposed ranking strategies.
2020年02期 v.25 161-179页 [查看摘要][在线阅读][下载 2324K] [下载次数：25 ] |[网刊下载次数：0 ] |[引用频次：2 ] |[阅读次数：0 ] - Zheng Wang;Chaokun Wang;Xiaojun Ye;Jisheng Pei;Bin Li;
Information diffusion is one of the most important issues in social network analysis.Unlike most existing works,which either rely on network topology or node profiles,this study focuses on the diffusion itself,i.e.,the recorded propagation histories.These histories are the evidence of diffusion and can be used to explain to users what happened in their networks.However,these histories can quickly grow in size and complexity,limiting their capacity to be intuitively understood.To reduce this information overload,in this paper we present the problem of propagation history ranking.The goal is to rank participant edges/nodes by their contribution to the diffusion.We first discuss and adapt a causal measure,Difference of Causal Effects(DCE),as the ranking criterion.Then,to avoid the complex calculation of DCE,we propose two integrated ranking strategies by adopting two indicators.One is responsibility,which captures the necessity aspect of causal effects.We further give an approximate algorithm,which could guarantee a feasible solution,for this indicator.The other is capability,which captures the sufficiency aspect of causal effects.Finally,promising experimental results are presented to verify the feasibility of the proposed ranking strategies.
2020年02期 v.25 161-179页 [查看摘要][在线阅读][下载 2324K] [下载次数：25 ] |[网刊下载次数：0 ] |[引用频次：2 ] |[阅读次数：0 ] - Jiang Zhang;Yufeng Wang;Zhiyuan Yuan;Qun Jin;
With the eruption of big data,practical recommendation schemes are now very important in various fields,including e-commerce,social networks,and a number of web-based services.Nowadays,there exist many personalized movie recommendation schemes utilizing publicly available movie datasets(e.g.,MovieLens and Netflix),and returning improved performance metrics(e.g.,Root-Mean-Square Error(RMSE)).However,two fundamental issues faced by movie recommendation systems are still neglected:first,scalability,and second,practical usage feedback and verification based on real implementation.In particular,Collaborative Filtering(CF)is one of the major prevailing techniques for implementing recommendation systems.However,traditional CF schemes suffer from a time complexity problem,which makes them bad candidates for real-world recommendation systems.In this paper,we address these two issues.Firstly,a simple but high-efficient recommendation algorithm is proposed,which exploits users' profile attributes to partition them into several clusters.For each cluster,a virtual opinion leader is conceived to represent the whole cluster,such that the dimension of the original useritem matrix can be significantly reduced,then a Weighted Slope One-VU method is designed and applied to the virtual opinion leader-item matrix to obtain the recommendation results.Compared to traditional clusteringbased CF recommendation schemes,our method can significantly reduce the time complexity,while achieving comparable recommendation performance.Furthermore,we have constructed a real personalized web-based movie recommendation system,MovieWatch,opened it to the public,collected user feedback on recommendations,and evaluated the feasibility and accuracy of our system based on this real-world data.
2020年02期 v.25 180-191页 [查看摘要][在线阅读][下载 1532K] [下载次数：263 ] |[网刊下载次数：0 ] |[引用频次：30 ] |[阅读次数：0 ] - Jiang Zhang;Yufeng Wang;Zhiyuan Yuan;Qun Jin;
With the eruption of big data,practical recommendation schemes are now very important in various fields,including e-commerce,social networks,and a number of web-based services.Nowadays,there exist many personalized movie recommendation schemes utilizing publicly available movie datasets(e.g.,MovieLens and Netflix),and returning improved performance metrics(e.g.,Root-Mean-Square Error(RMSE)).However,two fundamental issues faced by movie recommendation systems are still neglected:first,scalability,and second,practical usage feedback and verification based on real implementation.In particular,Collaborative Filtering(CF)is one of the major prevailing techniques for implementing recommendation systems.However,traditional CF schemes suffer from a time complexity problem,which makes them bad candidates for real-world recommendation systems.In this paper,we address these two issues.Firstly,a simple but high-efficient recommendation algorithm is proposed,which exploits users' profile attributes to partition them into several clusters.For each cluster,a virtual opinion leader is conceived to represent the whole cluster,such that the dimension of the original useritem matrix can be significantly reduced,then a Weighted Slope One-VU method is designed and applied to the virtual opinion leader-item matrix to obtain the recommendation results.Compared to traditional clusteringbased CF recommendation schemes,our method can significantly reduce the time complexity,while achieving comparable recommendation performance.Furthermore,we have constructed a real personalized web-based movie recommendation system,MovieWatch,opened it to the public,collected user feedback on recommendations,and evaluated the feasibility and accuracy of our system based on this real-world data.
2020年02期 v.25 180-191页 [查看摘要][在线阅读][下载 1532K] [下载次数：263 ] |[网刊下载次数：0 ] |[引用频次：30 ] |[阅读次数：0 ] - Yong Jiang;Jiong Cai;Kewei Tu;
Discriminative approaches have shown their effectiveness in unsupervised dependency parsing.However,due to their strong representational power,discriminative approaches tend to quickly converge to poor local optima during unsupervised training.In this paper,we tackle this problem by drawing inspiration from robust deep learning techniques.Specifically,we propose robust unsupervised discriminative dependency parsing,a framework that integrates the concepts of denoising autoencoders and conditional random field autoencoders.Within this framework,we propose two types of sentence corruption mechanisms as well as a posterior regularization method for robust training.We tested our methods on eight languages and the results show that our methods lead to significant improvements over previous work.
2020年02期 v.25 192-202页 [查看摘要][在线阅读][下载 1186K] [下载次数：16 ] |[网刊下载次数：0 ] |[引用频次：1 ] |[阅读次数：0 ] - Yong Jiang;Jiong Cai;Kewei Tu;
Discriminative approaches have shown their effectiveness in unsupervised dependency parsing.However,due to their strong representational power,discriminative approaches tend to quickly converge to poor local optima during unsupervised training.In this paper,we tackle this problem by drawing inspiration from robust deep learning techniques.Specifically,we propose robust unsupervised discriminative dependency parsing,a framework that integrates the concepts of denoising autoencoders and conditional random field autoencoders.Within this framework,we propose two types of sentence corruption mechanisms as well as a posterior regularization method for robust training.We tested our methods on eight languages and the results show that our methods lead to significant improvements over previous work.
2020年02期 v.25 192-202页 [查看摘要][在线阅读][下载 1186K] [下载次数：16 ] |[网刊下载次数：0 ] |[引用频次：1 ] |[阅读次数：0 ] - Yong Dong;Juan Chen;Yuhua Tang;Junjie Wu;Huiquan Wang;Enqiang Zhou;
Reducing the energy consumption of the storage systems disk read/write requests plays an important role in improving the overall energy efficiency of high-performance computing systems.We propose a method to reduce disk energy consumption by delaying the dispatch of disk requests to the end of a time window,which we call time window-based lazy scheduling.We prove that sorting requests within a single time window can reduce the disk energy consumption,and we discuss the relationship between the size of the time window and the disk energy consumption,proving that the energy consumption is highly likely to decrease with increasing window size.To exploit this opportunity,we propose the Lazy Scheduling based Disk Energy Optimization(LSDEO) algorithm,which adopts a feedback method to periodically adjust the size of the time window,and minimizes the local disk energy consumption by sorting disk requests within each time window.We implement the LSDEO algorithm in an OS kernel and conduct both simulations and actual measurements on the algorithm,confirming that increasing the time window increases disk energy savings.When the average request arrival rate is 300 and the threshold of average request response time is 50 ms,LSDEO can yield disk energy savings of 21.5%.
2020年02期 v.25 203-216页 [查看摘要][在线阅读][下载 1944K] [下载次数：22 ] |[网刊下载次数：0 ] |[引用频次：4 ] |[阅读次数：0 ] - Yong Dong;Juan Chen;Yuhua Tang;Junjie Wu;Huiquan Wang;Enqiang Zhou;
Reducing the energy consumption of the storage systems disk read/write requests plays an important role in improving the overall energy efficiency of high-performance computing systems.We propose a method to reduce disk energy consumption by delaying the dispatch of disk requests to the end of a time window,which we call time window-based lazy scheduling.We prove that sorting requests within a single time window can reduce the disk energy consumption,and we discuss the relationship between the size of the time window and the disk energy consumption,proving that the energy consumption is highly likely to decrease with increasing window size.To exploit this opportunity,we propose the Lazy Scheduling based Disk Energy Optimization(LSDEO) algorithm,which adopts a feedback method to periodically adjust the size of the time window,and minimizes the local disk energy consumption by sorting disk requests within each time window.We implement the LSDEO algorithm in an OS kernel and conduct both simulations and actual measurements on the algorithm,confirming that increasing the time window increases disk energy savings.When the average request arrival rate is 300 and the threshold of average request response time is 50 ms,LSDEO can yield disk energy savings of 21.5%.
2020年02期 v.25 203-216页 [查看摘要][在线阅读][下载 1944K] [下载次数：22 ] |[网刊下载次数：0 ] |[引用频次：4 ] |[阅读次数：0 ] - Qianwen Miao;Fu Xiao;Haiping Huang;Lijuan Sun;Ruchuan Wang;
Staff attendance information has always been an important part of corporate management.However,some opportunistic employees may consign others to punch their time cards,which hampers the authenticity of attendance and effectiveness of record keeping.Hence,it is necessary to develop an innovative anti-cheating system for office attendance.Radio-Frequency IDentification(RFID) offers new solutions to solve such problems because of its strong anti-interference capability and non-intrusiveness.In this paper,we present a smart attendance system that extracts distinguishable phase characteristics of individuals to enable recognition of various targets.A frequency distribution histogram is extracted as a fingerprint for recognition and the K-means clustering method is utilized for more fine-grained recognition of targets with similar features.Compared with traditional attendance mechanisms,RFID-based attendance systems are based on living biological characteristics,which greatly reduces the possibility of false records.To evaluate the performance of our system,we conducted extensive experiments.The results of which demonstrate the efficiency and accuracy of our system with an average accuracy of 92%.Moreover,the system evaluation shows that our design is robust against differences in the clothing worn and time of day,which further verifies the successful performance of our system.
2020年02期 v.25 217-226页 [查看摘要][在线阅读][下载 1316K] [下载次数：158 ] |[网刊下载次数：0 ] |[引用频次：10 ] |[阅读次数：0 ] - Qianwen Miao;Fu Xiao;Haiping Huang;Lijuan Sun;Ruchuan Wang;
Staff attendance information has always been an important part of corporate management.However,some opportunistic employees may consign others to punch their time cards,which hampers the authenticity of attendance and effectiveness of record keeping.Hence,it is necessary to develop an innovative anti-cheating system for office attendance.Radio-Frequency IDentification(RFID) offers new solutions to solve such problems because of its strong anti-interference capability and non-intrusiveness.In this paper,we present a smart attendance system that extracts distinguishable phase characteristics of individuals to enable recognition of various targets.A frequency distribution histogram is extracted as a fingerprint for recognition and the K-means clustering method is utilized for more fine-grained recognition of targets with similar features.Compared with traditional attendance mechanisms,RFID-based attendance systems are based on living biological characteristics,which greatly reduces the possibility of false records.To evaluate the performance of our system,we conducted extensive experiments.The results of which demonstrate the efficiency and accuracy of our system with an average accuracy of 92%.Moreover,the system evaluation shows that our design is robust against differences in the clothing worn and time of day,which further verifies the successful performance of our system.
2020年02期 v.25 217-226页 [查看摘要][在线阅读][下载 1316K] [下载次数：158 ] |[网刊下载次数：0 ] |[引用频次：10 ] |[阅读次数：0 ] - Ying Jin;Weiguang Guo;Yiwen Zhang;
Dynamic Quality of Service(QoS) prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and composition.Our paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time slice.In the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA) is applied to predict the missing values.In the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.
2020年02期 v.25 227-238页 [查看摘要][在线阅读][下载 1766K] [下载次数：36 ] |[网刊下载次数：0 ] |[引用频次：14 ] |[阅读次数：0 ] - Ying Jin;Weiguang Guo;Yiwen Zhang;
Dynamic Quality of Service(QoS) prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and composition.Our paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time slice.In the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA) is applied to predict the missing values.In the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.
2020年02期 v.25 227-238页 [查看摘要][在线阅读][下载 1766K] [下载次数：36 ] |[网刊下载次数：0 ] |[引用频次：14 ] |[阅读次数：0 ] - Xuemei Zeng;Xingshu Chen;Guolin Shao;Tao He;Lina Wang;
An increasing number of websites are making use of HTTPS encryption to enhance security and privacy for their users.However,HTTPS encryption makes it very difficult to identify the service over HTTPS flows,which poses challenges to network security management.In this paper we present DTA-HOC,a novel DNS-based two-level association HTTPS traffic online service identification method for large-scale networks,which correlates HTTPS flows with DNS flows using big data stream processing and association technologies to label the service in an HTTPS flow with a specific associated domain name.DTA-HOC has been specifically designed to address three practical challenges in the service identification process:domain name ambiguity,domain name query invisibility,and data association time window size contradictions.Several experiments on datasets collected from a 10-Gbps campus network are conducted alongside offline and online testing.Results show that DTA-HOC can achieve an average online association rate on HTTPS traffic of 83% and a generic accuracy of 86.16%.Its processing time for one minute of data is less than 20 seconds.These results indicate that DTA-HOC is an efficient method for online identification of services in HTTPS flows for large-scale networks.Moreover,our proposed method can contribute to the identification of other applications which make a Domain Name System(DNS) communication before establishing a connection.
2020年02期 v.25 239-254页 [查看摘要][在线阅读][下载 1990K] [下载次数：66 ] |[网刊下载次数：0 ] |[引用频次：2 ] |[阅读次数：0 ] - Xuemei Zeng;Xingshu Chen;Guolin Shao;Tao He;Lina Wang;
An increasing number of websites are making use of HTTPS encryption to enhance security and privacy for their users.However,HTTPS encryption makes it very difficult to identify the service over HTTPS flows,which poses challenges to network security management.In this paper we present DTA-HOC,a novel DNS-based two-level association HTTPS traffic online service identification method for large-scale networks,which correlates HTTPS flows with DNS flows using big data stream processing and association technologies to label the service in an HTTPS flow with a specific associated domain name.DTA-HOC has been specifically designed to address three practical challenges in the service identification process:domain name ambiguity,domain name query invisibility,and data association time window size contradictions.Several experiments on datasets collected from a 10-Gbps campus network are conducted alongside offline and online testing.Results show that DTA-HOC can achieve an average online association rate on HTTPS traffic of 83% and a generic accuracy of 86.16%.Its processing time for one minute of data is less than 20 seconds.These results indicate that DTA-HOC is an efficient method for online identification of services in HTTPS flows for large-scale networks.Moreover,our proposed method can contribute to the identification of other applications which make a Domain Name System(DNS) communication before establishing a connection.
2020年02期 v.25 239-254页 [查看摘要][在线阅读][下载 1990K] [下载次数：66 ] |[网刊下载次数：0 ] |[引用频次：2 ] |[阅读次数：0 ] - Haiming Huang;Junhao Lin;Linyuan Wu;Bin Fang;Zhenkun Wen;Fuchun Sun;
This paper focuses on multi-modal Information Perception(IP) for Soft Robotic Hands(SRHs) using Machine Learning(ML) algorithms.A flexible Optical Fiber-based Curvature Sensor(OFCS) is fabricated,consisting of a Light-Emitting Diode(LED),photosensitive detector,and optical fiber.Bending the roughened optical fiber generates lower light intensity,which reflecting the curvature of the soft finger.Together with the curvature and pressure information,multi-modal IP is performed to improve the recognition accuracy.Recognitions of gesture,object shape,size,and weight are implemented with multiple ML approaches,including the Supervised Learning Algorithms(SLAs) of K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Logistic Regression(LR),and the unSupervised Learning Algorithm(un-SLA) of K-Means Clustering(KMC).Moreover,Optical Sensor Information(OSI),Pressure Sensor Information(PSI),and Double-Sensor Information(DSI) are adopted to compare the recognition accuracies.The experiment results demonstrate that the proposed sensors and recognition approaches are feasible and effective.The recognition accuracies obtained using the above ML algorithms and three modes of sensor information are higer than 85 percent for almost all combinations.Moreover,DSI is more accurate when compared to single modal sensor information and the KNN algorithm with a DSI outperforms the other combinations in recognition accuracy.
2020年02期 v.25 255-269页 [查看摘要][在线阅读][下载 3080K] [下载次数：106 ] |[网刊下载次数：0 ] |[引用频次：12 ] |[阅读次数：0 ] - Haiming Huang;Junhao Lin;Linyuan Wu;Bin Fang;Zhenkun Wen;Fuchun Sun;
This paper focuses on multi-modal Information Perception(IP) for Soft Robotic Hands(SRHs) using Machine Learning(ML) algorithms.A flexible Optical Fiber-based Curvature Sensor(OFCS) is fabricated,consisting of a Light-Emitting Diode(LED),photosensitive detector,and optical fiber.Bending the roughened optical fiber generates lower light intensity,which reflecting the curvature of the soft finger.Together with the curvature and pressure information,multi-modal IP is performed to improve the recognition accuracy.Recognitions of gesture,object shape,size,and weight are implemented with multiple ML approaches,including the Supervised Learning Algorithms(SLAs) of K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Logistic Regression(LR),and the unSupervised Learning Algorithm(un-SLA) of K-Means Clustering(KMC).Moreover,Optical Sensor Information(OSI),Pressure Sensor Information(PSI),and Double-Sensor Information(DSI) are adopted to compare the recognition accuracies.The experiment results demonstrate that the proposed sensors and recognition approaches are feasible and effective.The recognition accuracies obtained using the above ML algorithms and three modes of sensor information are higer than 85 percent for almost all combinations.Moreover,DSI is more accurate when compared to single modal sensor information and the KNN algorithm with a DSI outperforms the other combinations in recognition accuracy.
2020年02期 v.25 255-269页 [查看摘要][在线阅读][下载 3080K] [下载次数：106 ] |[网刊下载次数：0 ] |[引用频次：12 ] |[阅读次数：0 ] - Mario Calín Sánchez;Juan Manuel Carrillo de Gea;José Luis Fernández-Alemán;Jesús Garcerán;Ambrosio Toval;
Computer security is a matter of great interest.In the last decade there have been numerous cases of cybercrime based on the exploitation of software vulnerabilities.This fact has generated a great social concern and a greater importance of computer security as a discipline.In this work,the most important vulnerabilities of recent years are identified,classified,and categorized individually.A measure of the impact of each vulnerability is used to carry out this classification,considering the number of products affected by each vulnerability,as well as its severity.In addition,the categories of vulnerabilities that have the greatest presence are identified.Based on the results obtained in this study,we can understand the consequences of the most common vulnerabilities,which software products are affected,how to counteract these vulnerabilities,and what their current trend is.
2020年02期 v.25 270-280页 [查看摘要][在线阅读][下载 1482K] [下载次数：49 ] |[网刊下载次数：0 ] |[引用频次：6 ] |[阅读次数：0 ] - Mario Calín Sánchez;Juan Manuel Carrillo de Gea;José Luis Fernández-Alemán;Jesús Garcerán;Ambrosio Toval;
Computer security is a matter of great interest.In the last decade there have been numerous cases of cybercrime based on the exploitation of software vulnerabilities.This fact has generated a great social concern and a greater importance of computer security as a discipline.In this work,the most important vulnerabilities of recent years are identified,classified,and categorized individually.A measure of the impact of each vulnerability is used to carry out this classification,considering the number of products affected by each vulnerability,as well as its severity.In addition,the categories of vulnerabilities that have the greatest presence are identified.Based on the results obtained in this study,we can understand the consequences of the most common vulnerabilities,which software products are affected,how to counteract these vulnerabilities,and what their current trend is.
2020年02期 v.25 270-280页 [查看摘要][在线阅读][下载 1482K] [下载次数：49 ] |[网刊下载次数：0 ] |[引用频次：6 ] |[阅读次数：0 ] - Yili Ren;Renbin Gong;Zhou Feng;Meichao Li;
Imaging logging has become a popular means of well logging because it can visually represent the lithologic and structural characteristics of strata.The manual interpretation of imaging logging is affected by the limitations of the naked eye and experiential factors.As a result,manual interpretation accuracy is low.Therefore,it is highly useful to develop effective automatic imaging logging interpretation by machine learning.Resistivity imaging logging is the most widely used technology for imaging logging.In this paper,we propose an automatic extraction procedure for the geological features in resistivity imaging logging images.This procedure is based on machine learning and achieves good results in practical applications.Acknowledging that the existence of valueless data significantly affects the recognition effect,we propose three strategies for the identification of valueless data based on binary classification.We compare the effect of the three strategies both on an experimental dataset and in a production environment,and find that the merging method is the best performing of the three strategies.It effectively identifies the valueless data in the well logging images,thus significantly improving the automatic recognition effect of geological features in resistivity logging images.
2020年02期 v.25 281-293页 [查看摘要][在线阅读][下载 2065K] [下载次数：125 ] |[网刊下载次数：0 ] |[引用频次：17 ] |[阅读次数：0 ] - Yili Ren;Renbin Gong;Zhou Feng;Meichao Li;
Imaging logging has become a popular means of well logging because it can visually represent the lithologic and structural characteristics of strata.The manual interpretation of imaging logging is affected by the limitations of the naked eye and experiential factors.As a result,manual interpretation accuracy is low.Therefore,it is highly useful to develop effective automatic imaging logging interpretation by machine learning.Resistivity imaging logging is the most widely used technology for imaging logging.In this paper,we propose an automatic extraction procedure for the geological features in resistivity imaging logging images.This procedure is based on machine learning and achieves good results in practical applications.Acknowledging that the existence of valueless data significantly affects the recognition effect,we propose three strategies for the identification of valueless data based on binary classification.We compare the effect of the three strategies both on an experimental dataset and in a production environment,and find that the merging method is the best performing of the three strategies.It effectively identifies the valueless data in the well logging images,thus significantly improving the automatic recognition effect of geological features in resistivity logging images.
2020年02期 v.25 281-293页 [查看摘要][在线阅读][下载 2065K] [下载次数：125 ] |[网刊下载次数：0 ] |[引用频次：17 ] |[阅读次数：0 ] - Peirong Fan;Xiaowei Cui;Mingquan Lu;
Diversity reception of multipath Global Navigation Satellte System(GNSS) signals offers a new insight into carrier phase-based high-precision positioning.The focus of this paper is to demonstrate the fading independence between space and frequency diversity GNSS signals.In harsh urban environments,multipath components arrive to the mobile receiver antenna with different phases and Doppler shifts,therefore giving rise to the discontinuity of code and Doppler observations and large tracking errors.In this paper,an empirical model of fading GNSS signals is constructed,including power fluctuations and spread metrics.Based on this model,real BeiDou Navigation Satellite System(BDS) signals from two GNSS dual-frequency antennas are characterized,at both information and signal level.The block processing algorithm is utilized for signal investigation.Results show that:(1) a high proportion of asynchronous loss-of-lock(around 16%) is experienced by observations of diversity signals;and(2) power fluctuations of fading signals are uncorrelated in frequency separated branches unconditionally,yet for space diversity signals the independency exists in dynamic fading channels only.The results above corroborate the significant potential gain of diversity reception,and could be further implemented in researches of diversity combined GNSS parameter estimation in dense fading conditions.
2020年02期 v.25 294-301页 [查看摘要][在线阅读][下载 1330K] [下载次数：37 ] |[网刊下载次数：0 ] |[引用频次：5 ] |[阅读次数：0 ] - Peirong Fan;Xiaowei Cui;Mingquan Lu;
Diversity reception of multipath Global Navigation Satellte System(GNSS) signals offers a new insight into carrier phase-based high-precision positioning.The focus of this paper is to demonstrate the fading independence between space and frequency diversity GNSS signals.In harsh urban environments,multipath components arrive to the mobile receiver antenna with different phases and Doppler shifts,therefore giving rise to the discontinuity of code and Doppler observations and large tracking errors.In this paper,an empirical model of fading GNSS signals is constructed,including power fluctuations and spread metrics.Based on this model,real BeiDou Navigation Satellite System(BDS) signals from two GNSS dual-frequency antennas are characterized,at both information and signal level.The block processing algorithm is utilized for signal investigation.Results show that:(1) a high proportion of asynchronous loss-of-lock(around 16%) is experienced by observations of diversity signals;and(2) power fluctuations of fading signals are uncorrelated in frequency separated branches unconditionally,yet for space diversity signals the independency exists in dynamic fading channels only.The results above corroborate the significant potential gain of diversity reception,and could be further implemented in researches of diversity combined GNSS parameter estimation in dense fading conditions.
2020年02期 v.25 294-301页 [查看摘要][在线阅读][下载 1330K] [下载次数：37 ] |[网刊下载次数：0 ] |[引用频次：5 ] |[阅读次数：0 ] - Rui Zhu;Tao Li;Jianxin Guo;Yangchao Huang;
Relay selection for Relay Assisted(RA) networks is an economical and effective method to improve the spectrum efficiency.Relay selection performs especially well when the source node has accurate and timely Channel State Information(CSI).However,since perfect CSI knowledge is rarely available,research of relay selection with partial(statistical) CSI is of paramount importance.In this paper,relay selection for RA networks with statistical CSI is formulated as a Multiple-Decision(MD) problem.And,the cost of obtaining the CSI is also considered in the formulated problem.Two relay selection schemes,Maximal Selection Probability(MSP)and Maximal Spectrum Efficiency Expectation(MSEE),are proposed to solve the formulated MD problem under different optimal criteria assumptions based on the optimal stopping theory.The MSP scheme maximizes the probability that the Best Assisted Relay Candidate(BARC) can be selected,whereas the MSEE scheme provides the maximal expectation of the spectrum efficiency.Experimental results show that the proposed schemes effectively improve the spectrum efficiency,and the MSEE scheme is more suitable for stable communication cases.Meanwhile,the MSP scheme is more suitable for burst communication cases.
2020年02期 v.25 302-312页 [查看摘要][在线阅读][下载 1823K] [下载次数：34 ] |[网刊下载次数：0 ] |[引用频次：23 ] |[阅读次数：0 ] - Rui Zhu;Tao Li;Jianxin Guo;Yangchao Huang;
Relay selection for Relay Assisted(RA) networks is an economical and effective method to improve the spectrum efficiency.Relay selection performs especially well when the source node has accurate and timely Channel State Information(CSI).However,since perfect CSI knowledge is rarely available,research of relay selection with partial(statistical) CSI is of paramount importance.In this paper,relay selection for RA networks with statistical CSI is formulated as a Multiple-Decision(MD) problem.And,the cost of obtaining the CSI is also considered in the formulated problem.Two relay selection schemes,Maximal Selection Probability(MSP)and Maximal Spectrum Efficiency Expectation(MSEE),are proposed to solve the formulated MD problem under different optimal criteria assumptions based on the optimal stopping theory.The MSP scheme maximizes the probability that the Best Assisted Relay Candidate(BARC) can be selected,whereas the MSEE scheme provides the maximal expectation of the spectrum efficiency.Experimental results show that the proposed schemes effectively improve the spectrum efficiency,and the MSEE scheme is more suitable for stable communication cases.Meanwhile,the MSP scheme is more suitable for burst communication cases.
2020年02期 v.25 302-312页 [查看摘要][在线阅读][下载 1823K] [下载次数：34 ] |[网刊下载次数：0 ] |[引用频次：23 ] |[阅读次数：0 ] 下载本期数据