8166991
On Destination Set in IP-Level Topology Measurement
We present a thorough investigation of four issues with the destination set in IP-level topology measurement. (1) To evaluate the completeness of the destination set, we measured by brute force and found that a half of edges will be missed by the uniform random selection (URS) method which was adopted by well-known CAIDA' skitter. (2) Two potential reasons for edge missing, the short-term routing dynamics and the subnetting technique, were investigated with the last-hop selection method and the bisection selection method respectively. And the results showed that the subnetting technique is the main reason. (3) To select destinations efficiently, we presented the minimum cover selection (MCS) method which performed better than the URS method on 75% of networks over three months. (4) Finally, to investigate the bias of topology characterization, we measured a destination set larger than before and discovered a Chinese topology whose size is 5 times as large as that discovered by skitter. The comparison between characteristics of skitter's topology and ours indicated that the small destination set trends to miss the information on marginal networks.
20797286
A Generic Validation Framework for Wideband MIMO Channel Models
In this paper, a generic framework for validating wideband MIMO channel models based on channel measurement results is proposed. The framework is formulated as a series of continuous functions (metrics) and a definition of distance of continuous function space (degree of approximation). The metrics characterize the MIMO channel from different perspectives, and the distance provides a quantitative measure of the degree of approximation for the specified model. Several fundamental metrics which reflect the spatial multiplexing gain, diversity capability, time and frequency variability, are derived for exploring the frequency-selective fading property. Based on an extensive measurement campaign at 5.25 GHz, the propagation channel is reconstructed by a WINNER-like model. The metrics are calculated from both the model generated channel realizations and the measured impulse response as a demonstration. The proposed framework can be applied to compare different channel models and to evaluate the simplified version of channel models.
42969353
Impact of Randomized Cross-Polarization Discrimination on Channel Correlation Property of the 3GPP Spatial Channel Model
The use of cross-polarized antennas for multiple-input multiple-output (MIMO) systems is receiving attention as they are able to double the number of antenna for half antenna spacing needs. This paper presents the channel correlation property of the 3rd Generation Partner Project (3GPP)/3GPP2 spatial channel model (SCM) with the polarization propagation. The statistical average of the per path polarization correlation given random cross-polarization discrimination (XPD) with co-located ideal tilted dipole antennas is derived. The impact on the random behavior of the polarization correlation due to the slant offset angle, the per path angular spread (AS), and the random XPD is analyzed. The simulation results show that the variation of polarization correlation caused by the random XPD is maximized with a 58° slant offset angle under the assumptions of all predefined scenarios in SCM. The per path AS has minor impact on the statistics of the polarization correlations. The randomness of polarization correlation is negligible for an XPD with small standard deviation.
8300404
Modeling Internet Link Delay Based on Measurement
Understanding Internet link delay is an important key of taking full advantage of Internet resources. In this paper, we find that the link delay follows Gammadistribution using Maximum Likelihood Estimation based on data measured in a large-scale network. The correlations between the link delay and the edge load, the node degree, and the betweenness are analyzed respectively. The link delay distribution parameters for different ranges of edge load are calculated. We findthat the edge load is correlated positively with the shape parameter of link delay Gamma distribution and negatively with the scale parameter.
41646301
A Comparison of Broadcast Strategy in MIMO Relay Networks
In this paper, the sum rate issue of a multiple-input multiple-output (MIMO) relay broadcast channel is studied, where the fixed relay station is used to support multiuser transmission in a downlink channel. With appropriate design, the sum rate can be expressed as a function of the processing units at the base station (BS) and relay, and the power allocation can be converted to a standard convex problem. We compared the sum rate of MIMO relay broadcast channel (BC) using time division multiple access (TDMA) to the strongest user, dirty paper coding (DPC) and beamforming (BF) when multiple antennas are deployed at each terminal in the system and separate power constraints at the BS and relay. Then, the sum rate bounds for each strategy and the sum capacity (achieved by DPC) gain over TDMA and BF are given, correspondingly. Finally, numerical results are presented to prove our discussions. With these results, we can easily obtain how far away TDMA and BF are from being optimal in terms of sum rate, and direct the practical system design.
1947863
Eigenvalue Statistics and Spatial Characteristics in Hotspot Areas Based on Wideband MIMO Channel Measurements
Wideband channel measurements have been performed at 5.25 GHz in hotspot areas in Beijing with a multiple- input multiple-output (MIMO) channel sounder. Based on the measured data in the indoor line-of-sight (LOS) and outdoor LOS/obstructed-line-of-sight (OLOS) scenarios, the frequency- dependent eigenvalues and average eigenvalue statistics are presented in this paper. In the indoor scenario, the probability density functions (PDFS) of the maximum and minimum eigenvalues are well fitted using the normal distribution. In the outdoor scenario, the log-normal distribution is proposed to model the pdfs of these two eigenvalues. As the indicator of spatial selectivity, the empirical cumulative distribution functions (CDFS) of condition number are obtained in two scenarios, indicating that the spatial multiplexing technique is more suitable in indoor LOS scenario than in the outdoor LOS/OLOS scenario. Additionally, the three-dimensional (3D) directional spread is calculated to illustrate the spatial dispersion of the multipath components. These information are of significance for the design and performance evaluation of MIMO communication systems employed in hotspot areas.
31519303
Data-Driven Facial Feature Morphing for 3D Face Synthesis
15125228
Web Mining for Lexical Context-Specific Paraphrasing
In most applications of paraphrasing, contextual information should be considered since a word may have different paraphrases in different contexts. This paper presents a method that automatically acquires lexical context-specific paraphrases from the web. The method includes two main stages, candidate paraphrase extraction and paraphrase validation. Evaluations were conducted on a news title corpus whereby the context-specific paraphrasing method was compared with the Chinese synonymous thesaurus. Results show that the precision of our method is above 60% and the recall is above 55%, which outperforms the thesaurus significantly.
15799724
An On-Off Queue Control Mechanism for Scalable Video Streaming over the IEEE 802.11e WLAN
In this paper, we study the issue of scalable video streaming over IEEE 802.11e EDCA WLANs. Our basic idea is to control the number of "active" nodes on the channel in order to reduce collisions under heavy traffic conditions. Specifically, we propose a distributed on-off queue control (OOQC) mechanism, which is designed to maintain high network throughput while keeping packet loss due to collision as low as possible. A low priority early drop (LPED) method is also employed to drop the packets at the queue according to packet relative priority index (RPI) provided by scalable video coding. Simulation results show that our proposed OOQC scheme significantly outperforms EDCA in received video quality.
9190159
Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene Expression Data
Abstract Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectation maximization (SEM) to model gene relationship. It is well-suited for analyzing the time-series data and can deal with cyclical structures that can not be tackled by static Bayesian network. We applied the new method to learning the regulatory network and the metabolic pathway from Saccharomyces Cerevisiae cell cycle gene expression data. The results show that the proposed method is capable of handling missing values in expression data sets, and the inference accuracy can further be improved. Keyword: Microarrays; Gene regulatory networks; Dynamic Bayesian network; Structural expectation maximization 1. Introduction The establishment of gene regulatory network is critical to the understanding of the genetic regulation process. This problem has become an important challenge in recent years. The invention of microarray technology is viewed as a milestone, which helps scientist measure expression levels of thousands of genes simultaneously. Several methods have been presented so far to learn gene network from microarray data, such as Boolean networks [2, 3], differential equations [4, 5], and Bayesian networks [6-8]. Among all of them, Bayesian network based approach has received a lot of attention because of the probabilistic nature of this model. It can be used to learn causal relationship and particularly, combine it with prior knowledge readily. However, there exist a lot of shortcomings for the static version of Bayesian network. First, it is unable to capture the temporal information. Second, it is impossible to model cyclic network, which is often considered to be an accurate description of real gene regulation mechanism. In this paper, we employ dynamic Bayesian network (DBN) [1, 9,10], instead of its earlier static version, to model a gene network with cyclic regulation. In general, the DBN is well-suited for characterizing time-series gene expression data. Owing to the limitation of experimental condition, there are many missing values in the gene expression data sets, which usually have an impact on the inference accuracy. To address this problem, we propose a new DBN model embedded with
1600088
Automatic Acquisition of Context-Specific Lexical Paraphrases
Lexical paraphrasing aims at acquiring word-level paraphrases. It is critical for many Natural Language Processing (NLP) applications, such as Question Answering (QA), Information Extraction (IE), and Machine Translation (MT). Since the meaning and usage of a word can vary in distinct contexts, different paraphrases should be acquired according to the contexts. However, most of the existing researches focus on constructing paraphrase corpora, in which little contextual constraints for paraphrase application are imposed. This paper presents a method that automatically acquires context-specific lexical paraphrases. In this method, the obtained paraphrases of a word depend on the specific sentence the word occurs in. Two stages are included, i.e. candidate paraphrase extraction and paraphrase validation, both of which are mainly based on web mining. Evaluations are conducted on a news title corpus and the presented method is compared with a paraphrasing method that exploits a Chinese thesaurus of synonyms -- Tongyi Cilin (Extended) (CilinE for short). Results show that the f-measure of our method (0.4852) is significantly higher than that using CilinE (0.1127). In addition, over 85% of the correct paraphrases derived by our method cannot be found in CilinE, which suggests that our method is effective in acquiring out-of-thesaurus paraphrases.