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Parametric channel modeling based OFDM channel estimation

The Journal of China Universities of Posts and Telecommunications 2014年第5期21卷 1-8页

作者： QING Hao-bo LIU Yuan-an XIE Gang LIU Kai-ming LIU Fang Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications

A simplified parametric channel estimation approach was proposed for orthogonal frequency division multiplexing （OFDM） systems. Based on parametric channel model, this algorithm is composed of two parts： the estimation of channel parameters and channel interpolation. The exponentially embedded family （EEF） criterion is exploited to determine the number of channel paths as well as the multipath time delays. Consequently, the channel frequency responses is acquired via the estimated parameters. Additionally, the authors＇ scheme is computationally efficient owing to the needless of the eigenvalue decomposition or the estimation of signal parameters by the rotational invariance technique （ESPRIT）. Simulations are provided to validate the performance of this algorithm from perspectives of the probability of correct estimation and the mean square error （MSE）. It is demonstrated that this approach exhibits a superior performance over the existing algorithms.

关键词： channel estimation OFDM mean square error EEF criterion

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Enhanced EVD based channel estimation and pilot decontamination for Massive MIMO networks

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The Journal of China Universities of Posts and Telecommunications 2015年第6期22卷 72-77页

作者： Guo Mangqing Xie Gang Gao Jinchun Liu Yuan'an Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications

The enhanced eigenvalue decomposition （EEVD） based channel estimation algorithm, which could solve the pilot contamination problem in massive multiple-input and multiple-output （Massive MIMO） channel estimation when the number of antennas at base stations （NABT） tends to infinity, is proposed in this paper. The algorithm is based on the close relationship between covariance matrix of received pilot signal and the channel fast fading coefficient matrix, i.e. the latter is the eigenvector matrix of the former when NABT tends to infinity. Therefore, we can get a set of normalized base vectors from the eigenvalue decomposition （EVD） of sample covariance matrix in practical Massive MIMO networks. By multiplying the received pilot signal with conjugate transpose of normalized base vector matrix, the channel matrix is projected to a lower dimensional matrix, and the intra-cell and inter-cell interference can be eliminated completely when NABT tends to infinity. Thus, we only need to estimate the lower dimensional projected matrix during the channel estimation. Simulation results show that the mean square error （MSE） performance of channel estimation is improved with approximately two orders of magnitude when the signal-to-noise ratio （SNR） is 40 dB, compared with EVD based channel estimation algorithm. And the signal-to-interference ratio （SIR） is improved greatly as well. The increment of SIR becomes larger and larger as SNR increasing.

关键词： channel estimation eigenvalue decomposition （EVD） Massive MIMO pilot contamination

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Timely traffic identification on P2P streaming media

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The Journal of China Universities of Posts and Telecommunications 2012年第2期19卷 67-73页

作者： YANG Jie YUAN Lun HE Yang CHEN Lu-ying Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China

Since the year of 2006, peer-to-peer （P2P） streaming media service has been developing rapidly, the user scale and income scale achieve synchronous growth. However, while people enjoying the benefits of the distributed resources, a great deal of network bandwidth is consumed at the same time. Research on P2P streaming traffic characteristics and identification is essential to Internet service providers （ISPs） in terms of network planning and resource allocation. In this paper, we introduce the current common P2P traffic detection technology, and analyze the payload length distribution and payload length pattern in one flow of four popular P2P streaming media applications. Combining with the deep flow inspection and machine learning algorithm, a nearly real-time The experiments proved that this approach can achieve a high identification approach for P2P streaming media is proposed. accuracy with low false positives.

关键词： deep flow inspection machine learning payload length distribution traffic identification

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Dynamic recovery for survivable virtual network embedding

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The Journal of China Universities of Posts and Telecommunications 2014年第3期21卷 77-84页

作者： LU Bo HUANG Tao SUN Xiao-chuan CHEN Jian-ya LIU Yun-jie Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications School of Information Engineering Hebei United University

network virtualization is a promising way to overcome the current ossification of the Intemet. It is essential challenge to find effective, efficient and robust embedding algorithms for recovering virtual network. The virtual network mapping algorithm based on integer programming which was proposed months ago. But it did consider the faults of physical network resources, which is so called survivable virtual network embedding （VNE） problem. Previous strategies for enabling survivability in network virtualization focused on providing protection for the physical network or enhancing the virtual networks by providing backup physical resources in advance, and treated all the physical failures as link failures. In the article, a dynamic recovery method is proposed to solve the survivable virtual network embedding problem based on the integer programming VNE algorithm. The dynamic recovery method doesn＇t need to backup physical resources and it makes more substrate resources which can be used in the embedding. The dynamic recovery process will be activated only when physical failures occur. Different algorithms are used to recovery node and link failures. Simulations show that the method helps to recover almost all of physical failures by finding the substitute nodes and paths, and its performance is very close to that of pure VNE method without considering physical failures.

关键词： network virtualization integer programming survivable virtual network embedding dynamic recovery

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Performance improvement for applying network virtualization in fiber-wireless(FiWi) access networks

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第11期15卷 1058-1070页

作者： Qing-long DAI Guo-chu SHOU Yi-hong HU Zhi-gang GUO Beijing Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications

Fiber-wireless(FiWi) access networks, which are a combination of fiber networks and wireless networks,have the advantages of both networks, such as high bandwidth, high security, low cost, and flexible access. However,with the increasing need for bandwidth and types of service from users, FiWi networks are still relatively incapable and ossified. To alleviate bandwidth tension and facilitate new service deployment, we attempt to apply network virtualization in FiWi networks, in which the network's control plane and data plane are separated from each *** on a previously proposed hierarchical model and service model for FiWi network virtualization, the process of service implementation is described. The performances of the FiWi access networks applying network virtualization are analyzed in detail, including bandwidth for links, throughput for nodes, and multipath flow *** results show that the FiWi network with virtualization is superior to that without.

关键词： FiWi access networks network virtulization Performance analysis

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Analysis and Prediction of Content Popularity for Online Video Service:A Youku Case Study

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China Communications 2016年第12期13卷 216-233页

作者： Chenyu Li Jun Liu Shuxin Ouyang Beijing Key Laboratory of Network System Architecture and Convergence Center for Data ScienceSchool of Information and Communication EngineeringBeijing University of Posts and Telecommunications

Understanding the characteristics and predicting the popularity of the newly published online videos can provide direct implications in various contexts such as service design, advertisement planning, network management and etc. In this paper, we collect a real-world large-scale dataset from a leading online video service provider in China, namely Youku. We first analyze the dynamics of content publication and content popularity for the online video service. Then, we propose a rich set of features and exploit various effective classification methods to estimate the future popularity level of an individual video in various scenarios. We show that the future popularity level of a video can be predicted even before the video's release, and by introducing the historical popularity information the prediction performance can be improved dramatically. In addition, we investigate the importance of each feature group and each feature in the popularity prediction, and further reveal the factors that may impact the video popularity. We also discuss how the early monitoring period influences the popularity level prediction. Our work provides an insight into the popularity of the newly published online videos, and demonstrates promising practical applications for content publishers,service providers, online advisers and network operators.

关键词： online content popularity online video service popularity characterization popularity prediction

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Analysis of prediction performance in wavelet minimum complexity echo state network

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The Journal of China Universities of Posts and Telecommunications 2013年第4期20卷 59-66页

作者： CUI Hong-yan FENG Chen LIU Yun-jie Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing University of Posts and Telecommunications

Echo state network （ESN） has become one of the most popular recurrent neural networks （RNN） for its good prediction performance of non-linear time series and simple training process. But several problems still prevent ESN from becoming a widely used tool. The most prominent problem is its high complexity with lots of random parameters. Aiming at this problem, a minimum complexity ESN model （MCESN） was proposed. In this paper, we proposed a new wavelet minimum complexity ESN model （WMCESN） to improve the prediction accuracy and increase the practical applicability. Our new model inherits the characters of minimum complexity ESN model using the fixed parameters and simple circle topology. We injected wavelet neurons to replace the original neurons in internal reservoir and designed a wavelet parameter matrix to reduce the computing time. By using different datasets, our new model performed better than the minimum complexity ESN model with normal neurons, but only utilized tiny time cost. We also used our own packets of transmission control protocol （TCP） and user datagram protocol （UDP） dataset to prove that our model can deal with the data packet bit prediction problem well.

关键词： wavelet minimum complexity echo state network echo state network wavelet parameter matrix practical applicability

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Offline traffic analysis system based on Hadoop

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The Journal of China Universities of Posts and Telecommunications 2013年第5期20卷 97-103页

作者： QIAO Yuan-yuan LEI Zhen-ming YUAN Lun GUO Min-jie Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Produce Ads Amazon Joyo Co. Ltd

Offiine network traffic analysis is very important for an in-depth study upon the understanding of network conditions and characteristics, such as user behavior and abnormal traffic. With the rapid growth of the amount of information on the Intemet, the traditional stand-alone analysis tools face great challenges in storage capacity and computing efficiency, but which is the advantages for Hadoop cluster. In this paper, we designed an offiine traffic analysis system based on Hadoop （OTASH）, and proposed a MapReduce-based algorithm for TopN user statistics. In addition, we studied the computing performance and failure tolerance in OTASH. From the experiments we drew the conclusion that OTASH is suitable for handling large amounts of flow data, and are competent to calculate in the case of single node failure.

关键词： MapReduce Hadoop cloud computing traffic analysis

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Doubly selective channel estimation and symbol detection with data-dependent superimposed training for OFDM systems

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Journal of Information and Computational Science 2012年第2期9卷 323-331页

作者： Zhang, Qinjuan Wu, Muqing Guo, Qilin Zhang, Tingting Zhang, Rui Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing 100876 China

A channel estimation using Data-dependent Superimposed Training (DDST) over doubly selective channel is proposed for OFDM systems. Instead of using traditional pilot-assisted scheme, this scheme uses training superimposed to data sequence in time domain to assist the estimation, which consumes no extra bandwidth. The superimposed training is designed data-dependent to eliminate the interference of unknown data on estimation thoroughly. Meanwhile, an iterative symbol detection method is presented for the DDST based time varying channel. Simulations show that the proposed estimator can approach the real channel more accurately and achieve better Bit Error Ratio (BER) performance. Copyright © 2012 Binary Information Press.

关键词： Channel estimation

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Design of in-network caching scheme in CCN based on grey relational analysis

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The Journal of China Universities of Posts and Telecommunications 2014年第2期21卷 1-8页

作者： CUI Xian-dong HUANG Tao LIU Jiang LI Li CHEN Jian-ya LIU Yun-jie State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Future Network Industry Innovation Center of China

In-network caching is one of the most important issues in content centric networking （CCN）, which may extremely influence the performance of the caching system. Although much work has been done for in-network caching scheme design in CCN, most of them have not addressed the multiple network attribute parameters jointly during caching algorithm design. Hence, to fill this gap, a new in-network caching based on grey relational analysis （GRA） is proposed. The authors firstly define two newly metric parameters named request influence degree （RID） and cache replacement rate, respectively. The RID indicates the importance of one node along the content delivery path from the view of the interest packets arriving The cache replacement rate is used to denote the caching load of the node. Then combining hops a request traveling from the users and the node traffic, four network attribute parameters are considered during the in-network caching algorithm design. Based on these four network parameters, a GRA based in-network caching algorithm is proposed, which can significantly improve the performance of CCN. Finally, extensive simulation based on ndnSIM is demonstrated that the GRA-based caching scheme can achieve the lower load in the source server and the less average hops than the existing the betweeness （Betw） scheme and the ALWAYS scheme.

关键词： CCN in-network caching request influence degree cache replacement rate grey relational analysis

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