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Joint Optimization of Satisfaction Index and Spectrum Efficiency with Cache Restricted for Resource Allocation in Multi-Beam Satellite Systems

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China Communications 2019年第2期16卷 189-201页

作者： Pei Zhang Xiaohui Wang Zhiguo Ma junde song School of Computer Science Beijing University of Posts and Telecommunications China Academy of Information and Communications Technology

Dynamic resource allocation(DRA) is a key technology to improve system performances in GEO multi-beam satellite systems. And, since the cache resource on the satellite is very valuable and limited, DRA problem under restricted cache resources is also an important issue to be studied. This paper mainly investigates the DRA problem of carrier resources under certain cache constraints. What's more, with the aim to satisfy all users' traffic demands as more as possible, and to maximize the utilization of the bandwidth, we formulate a multi-objective optimization problem(MOP) where the satisfaction index and the spectrum efficiency are jointly optimized. A modified strategy SA-NSGAII which combines simulated annealing(SA) and non-dominated sorted genetic algorithm-II(NSGAII) is proposed to approximate the Pareto solution to this MOP problem. Simulation results show the effectiveness of the proposed algorithm in terms of satisfaction index, spectrum efficiency, occupied cache, and etc.

关键词： GEO multi-beam satellite system dynamic resource allocation SA-NSGAII cache satisfaction index spectrum efficiency

Access network selection strategy using position prediction in heterogeneous wireless networks

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Frontiers of Electrical and Electronic Engineering in China 2010年第1期5卷 23-28页

作者： Chen GU Mei song Yong ZHANG junde song School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Access network selection(ANS)strategy is one of the most important issues in future heterogeneous networks.The current solutions for this issue are not very efficient because they do not consider the motion scenarios and cannot predict the next location for mobile node.In this paper,an effective ANS strategy based on global positioning system(GPS)is proposed.Making use of information such as position coordinates and moving velocity acquired by GPS,the ANS proposed can predict the next point of attachment for mobile node(MN),assist existing ANS strategy to make more reasonable decision,and achieve better performance.

关键词： position prediction access network selection(ANS) heterogeneous networks global positioning system(GPS)

Novel anomaly detection approach for telecommunication network proactive performance monitoring

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维普期刊数据库

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Frontiers of Electrical and Electronic Engineering in China 2009年第3期4卷 307-312页

作者： Yanhua YU Jun WANG Xiaosu ZHAN junde song School of Computer Science and Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

The mode of telecommunication network management is changing from“network oriented”to“subscriber oriented”.Aimed at enhancing subscribers’feeling,proactive performance monitoring(PPM)can enable a fast fault correction by detecting anomalies designating performance degradation.In this paper,a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average(ARIMA).Furthermore,under the assumption that the training residual is a white noise process following a normal distribution,the associated confidence interval of prediction can be figured out under any given confidence degree 1–αby constructing random variables satisfying t distribution.Experimental results verify the method’s effectiveness.

关键词： proactive performance monitoring(PPM) anomaly detection time series prediction autoregressive integrated moving average(ARIMA) white noise confidence interval

Improved Selective ACK Scheduling Optimization over Asymmetric Paths for CMT-SCTP

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Improved Selective ACK Scheduling Optimization over Asymmetr...

Joint model of user check-in activities for point-of-interest recommendation

2011 International Conference on Future Wireless Networks and Information Systems(ICFWI 2011)

作者： Jie Chang Wen’an Zhou junde song Beijing University of Posts and Telecommunications

With the deployment of more and more resilience critical Internet applications,there is a rising demand for multi-homed network sites.SCTP is a general-purpose Transport Layer protocol that provides multi-homing feature.Concurrent Multipath Transfer with SCTP’s multi-homing support（CMTSCTP）,which is the extension of SCTP protocol,simultaneously transmits new data from a source host to a destination host via two or more end-to-end paths.This paper proposes an improved Selective Acknowledgement（SACK） scheduling optimization scheme for improving the transmission throughput in the concurrent multipath transfer（CMT） environment consisting of asymmetric paths.In the proposed scheme,the main contribution is that a multi-homed receiver senses bandwidth values of all available reverse paths and then distributes SACK packets to each path proportionally.From the simulation test,we can see that when the end-to-end propagation delays of paths varied,the application throughput of the proposed scheme could improve the application throughput under bit error rate and delay varied.

关键词： Improved SACK Scheduling Optimization CMT-SCTP Asymmetric Links

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The Journal of China Universities of Posts and Telecommunications 2016年第4期23卷 25-36页

作者： Ren Xingyi song Meina E Haihong song junde School of Telecommunication Engineering Beijing University of Posts and Telecommunications

With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.

关键词： POI recommendation user check-in activities joint probabilistic generative model geographical influence social influence temporal effect content information popularity information

QoS prediction algorithm used in location-aware hybrid Web service

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The Journal of China Universities of Posts and Telecommunications 2015年第1期22卷 42-49页

作者： E Haihong Tong Junjie song Meina song junde School of Computer Science Beijing University of Posts and Telecommunications Engineering Research Center of Information Networks Ministry of Education

Quality-of-Service （QoS） describes the non-functional characteristics of Web services. As such, the QoS is a critical parameter in service selection, composition and fault tolerance, and must be accurately determined by some type of QoS prediction method. However, with the dramatic increase in the number of Web services, the prediction failure caused by data sparseness has become a critical challenge. A new ＇hybrid user-location-aware prediction based on weighted Adamic-Adar （WAA）＇（HUWAA） was proposed. The implicit neighbor search was optimized by incorporating location factors. Meanwhile, the ability of the improved algorithms to solve the data sparsity problem was validated in experiments on public real world datasets. The new algorithm outperforms the existing of item-based pearson correlation coefficient （IPCC）, user-based pearson correlation coefficient （UPCC） and Web service recommender system （WSRec） algorithms.

关键词： service QoS prediction data sparsity link prediction location-aware

Textual-geographical-social aware point-of-interest recommendation

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The Journal of China Universities of Posts and Telecommunications 2016年第6期23卷 24-33,67页

作者： Ren Xingyi song Meina E Haihong song junde School of Telecommunication Engineering Beijing University of Posts and Telecommunications Engineering Research Center of Information Networks Beijing University of Posts and Telecommunications

The rapid development of location-based social networks（LBSNs） has provided an unprecedented opportunity for better location-based services through point-of-interest（POI） recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper we propose a textual-geographical-social aware probabilistic matrix factorization method for POI recommendation. Our model is textual-geographical-social aware probabilistic matrix factorization called TGS-PMF, it exploits textual information, geographical information, social information, and incorporates these factors effectively. First, we exploit an aggregated latent Dirichlet allocation（LDA） model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, our exploit probabilistic matrix factorization model（PMF） to integrate the interest, geographical, social relevance scores for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGS-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.

关键词： location-based social networks POI recommendation topic model geographical correlations social correlations

Social media mining and visualization for point-o f-interest recommendation

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The Journal of China Universities of Posts and Telecommunications 2017年第1期24卷 67-76,86页

作者： Ren Xingyi song Meina E Haihong song junde School of Computing Beijing University of Posts and Telecommunications Beijing 100876 China Engineering Research Center of Information Networks Beijing University of Posts and Telecommunications Beijing 100876 China

With the rapid growth of location-based social networks （LBSNs）, point-of-interest （POI） recommendation has become an important research problem. As one of the most representative social media platforms, Twitter provides various real-life information for POI recommendation in real time. Despite that POI recommendation has been actively studied, tweet images have not been well utilized for this research problem. State-of-the-art visual features like convolutional neural network （CNN） features have shown significant performance gains over the traditional bag-of-visual-words in unveiling the image＇s semantics. Unfortunately, they have not been employed for POI recommendation from social websites. Hence, how to make the most of tweet images to improve the performance of POI recommendation and visualization remains open In this paper, we thoroughly study the impact of tweet images on POI recommendation for different POI categories using various visual features. A novel topic model called social media Twitter-latent Dirichlet allocation （SM-TwitterLDA） which jointly models five Twitter features, （i.e., text, image, location, timestamp and hashtag） is designed to discover POIs from the sheer amount of tweets. Moreover, each POI is visualized by representative images selected on three predefined criteria. Extensive experiments have been conducted on a real-life tweet dataset to verify the effectiveness of our method.

关键词： social media Twitter POI recommendation visualization

Vision-based positioning system

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The Journal of China Universities of Posts and Telecommunications 2016年第5期23卷 88-96页

作者： song Meina Ou Zhonghong E Haihong song junde Zhao Xuejun School of Computer Science Beijing University of Posts and Telecommunications

Conventional outdoor navigation systems are usually based on orbital satellites, e.g., global positioning system （GPS） and global navigation satellite system （GLONASS）. The latest advances from wearable, e.g., BaiduEye and Google Glass, have enabled new approaches to leverage information from the surrounding environment. For example, they enable the change from passively receiving information to actively requesting information. Thus, such changes might inspire brand new application scenarios that were not possible before. In this work, we propose a vision-based navigation system based on wearable like Baidu Eye. We discuss the associated challenges and propose potential solutions for each of them. The system utilizes crowd sensing to collect and build a traffic signpost database for positioning reference. Then it leverages context information, such as cell identification （Cell ID）, signal strength, and altitude combined with traffic sign detection and recognition to enable real-time positioning. A hybrid cloud architecture is proposed to enhance the capability of sensing devices （SD） to realize the proposed vision.

关键词： vision-based positioning system wearable machine vision