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Bin Dai

Publications and source records attributed to Bin Dai.

68 records · Page 4Linked to original sources
arXiv2015

Multiple-Access Relay Wiretap Channel

In this paper, we investigate the effects of an additional trusted relay node on the secrecy of multiple-access wiretap channel (MAC-WT) by considering the model of multiple-access relay wiretap channel (MARC-WT). More specifically, first, we investigate the discrete memoryless MARC-WT. Three inner bounds (with respect to decode-forward (DF), noise-forward (NF) and compress-forward (CF) strategies) on the secrecy capacity region are provided. Second, we investigate the degraded discrete memoryless MARC-WT, and present an outer bound on the secrecy capacity region of this degraded model. Finally, we investigate the Gaussian MARC-WT, and find that the NF and CF strategies help to enhance Tekin-Yener's achievable secrecy rate region of Gaussian MAC-WT. Moreover, we find that if the noise variance of the transmitters-relay channel is smaller than that of the transmitters-receiver channel, the DF strategy may also enhance Tekin-Yener's achievable secrecy rate region of Gaussian MAC-WT, and it may perform even better than the NF and CF strategies.

cs.IT↗
arXiv2015

Spectral imaging with dual compressed sensing

We experimentally demonstrated a spectral imaging scheme with dual compressed sensing. With the dimensions of spectral and spatial information both compressed, the spectral image of a colored object can be obtained with only a single point detector. The effect of spatial and spectral modulation numbers on the imaging quality is also analyzed. Our scheme provides a stable, highly consistent approach of spectral imaging.

physics.optics↗
arXiv2014

Taking A Free Ride for Routing Topology Inference in Peer-to-Peer Networks

A Peer-to-Peer (P2P) network can boost its performance if peers are provided with underlying network-layer routing topology. The task of inferring the network-layer routing topology and link performance from an end host to a set of other hosts is termed as network tomography, and it normally requires host computers to send probing messages. We design a passive network tomography method that does not requires any probing messages and takes a free ride over data flows in P2P networks. It infers routing topology based on end-to-end delay correlation estimation (DCE) without requiring any synchronization or cooperation from the intermediate routers. We implement and test our method in the real world Internet environment and achieved the accuracy of 92% in topology recovery. We also perform extensive simulation in OMNet++ to evaluate its performance over large scale networks, showing that its topology recovery accuracy is about 95% for large networks.

cs.NI↗
arXiv2014

A Survey on Network Tomography with Network Coding

The overhead of internal network monitoring motivates techniques of network tomography. Network coding (NC) presents a new opportunity for network tomography as NC introduces topology-dependent correlation that can be further exploited in topology estimation. Compared with traditional methods, network tomography with NC has many advantages such as the improvement of tomography accuracy and the reduction of complexity in choosing monitoring paths. In this paper we first introduce the problem of tomography with NC and then propose the taxonomy criteria to classify various methods. We also present existing solutions and future trend. We expect that our comprehensive review on network tomography with NC can serve as a good reference for researchers and practitioners working in the area.

cs.NI↗
arXiv2014

Bandwidth-Aware Scheduling with SDN in Hadoop: A New Trend for Big Data

Software Defined Networking (SDN) is a revolutionary network architecture that separates out network control functions from the underlying equipment and is an increasingly trend to help enterprises build more manageable data centers where big data processing emerges as an important part of applications. To concurrently process large-scale data, MapReduce with an open source implementation named Hadoop is proposed. In practical Hadoop systems one kind of issue that vitally impacts the overall performance is know as the NP-complete minimum make span problem. One main solution is to assign tasks on data local nodes to avoid link occupation since network bandwidth is a scarce resource. Many methodologies for enhancing data locality are proposed such as the HDS and state-of-the-art scheduler BAR. However, all of them either ignore allocating tasks in a global view or disregard available bandwidth as the basis for scheduling. In this paper we propose a heuristic bandwidth-aware task scheduler BASS to combine Hadoop with SDN. It is not only able to guarantee data locality in a global view but also can efficiently assign tasks in an optimized way. Both examples and experiments demonstrate that BASS has the best performance in terms of job completion time. To our knowledge, BASS is the first to exploit talent of SDN for big data processing and we believe it points out a new trend for large-scale data processing.

cs.DC↗
arXiv2013

Multivariate Bernoulli distribution

In this paper, we consider the multivariate Bernoulli distribution as a model to estimate the structure of graphs with binary nodes. This distribution is discussed in the framework of the exponential family, and its statistical properties regarding independence of the nodes are demonstrated. Importantly the model can estimate not only the main effects and pairwise interactions among the nodes but also is capable of modeling higher order interactions, allowing for the existence of complex clique effects. We compare the multivariate Bernoulli model with existing graphical inference models - the Ising model and the multivariate Gaussian model, where only the pairwise interactions are considered. On the other hand, the multivariate Bernoulli distribution has an interesting property in that independence and uncorrelatedness of the component random variables are equivalent. Both the marginal and conditional distributions of a subset of variables in the multivariate Bernoulli distribution still follow the multivariate Bernoulli distribution. Furthermore, the multivariate Bernoulli logistic model is developed under generalized linear model theory by utilizing the canonical link function in order to include covariate information on the nodes, edges and cliques. We also consider variable selection techniques such as LASSO in the logistic model to impose sparsity structure on the graph. Finally, we discuss extending the smoothing spline ANOVA approach to the multivariate Bernoulli logistic model to enable estimation of non-linear effects of the predictor variables.

stat.AP↗
arXiv2013

OEM for least squares problems

We propose an algorithm, called OEM (a.k.a. orthogonalizing EM), intended for var- ious least squares problems. The first step, named active orthogonization, orthogonalizes an arbi- trary regression matrix by elaborately adding more rows. The second step imputes the responses of the new rows. The third step solves the least squares problem of interest for the complete orthog- onal design. The second and third steps have simple closed forms, and iterate until convergence. The algorithm works for ordinary least squares and regularized least squares with the lasso, SCAD, MCP and other penalties. It has several attractive theoretical properties. For the ordinary least squares with a singular regression matrix, an OEM sequence converges to the Moore-Penrose gen- eralized inverse-based least squares estimator. For the SCAD and MCP, an OEM sequence can achieve the oracle property after sufficient iterations for a fixed or diverging number of variables. For ordinary and regularized least squares with various penalties, an OEM sequence converges to a point having grouping coherence for fully aliased regression matrices. Convergence and convergence rate of the algorithm are examined. These convergence rate results show that for the same data set, OEM converges faster for regularized least squares than ordinary least squares. This provides a new theoretical comparison between these methods. Numerical examples are provided to illustrate the proposed algorithm.

stat.CO↗
arXiv2013

DCE: A Novel Delay Correlation Measurement for Tomography with Passive Realization

Tomography is important for network design and routing optimization. Prior approaches require either precise time synchronization or complex cooperation. Furthermore, active tomography consumes explicit probeing resulting in limited scalability. To address the first issue we propose a novel Delay Correlation Estimation methodology named DCE with no need of synchronization and special cooperation. For the second issue we develop a passive realization mechanism merely using regular data flow without explicit bandwidth consumption. Extensive simulations in OMNeT++ are made to evaluate its accuracy where we show that DCE measured delay correlation is highly identical with the true value. Also from test result we find that mechanism of passive realization is able to achieve both regular data transmission and purpose of tomography with excellent robustness versus different background traffic and package size.

cs.NI↗
arXiv2012

Information-Theoretical Security for Several Models of Multiple-Access Channel

Several security models of multiple-access channel (MAC) are investigated. First, we study the degraded MAC with confidential messages, where two users transmit their confidential messages (no common message) to a destination, and each user obtains a degraded version of the output of the MAC. Each user views the other user as a eavesdropper, and wishes to keep its confidential message as secret as possible from the other user. Measuring each user's uncertainty about the other user's confidential message by equivocation, the inner and outer bounds on the capacity-equivocation region for this model have been provided. The result is further explained via the binary and Gaussian examples. Second, the discrete memoryless multiple-access wiretap channel (MAC-WT) is studied, where two users transmit their corresponding confidential messages (no common message) to a legitimate receiver, while an additional wiretapper wishes to obtain the messages via a wiretap channel. This new model is considered into two cases: the general MAC-WT with cooperative encoders, and the degraded MAC-WT with non-cooperative encoders. The capacity-equivocation region is totally determined for the cooperative case, and inner and outer bounds on the capacity-equivocation region are provided for the non-cooperative case. For both cases, the results are further explained via the binary examples.

cs.IT↗
arXiv2012

Degraded Broadcast Channel with Side Information, Confidential Messages and Noiseless Feedback

In this paper, first, we investigate the model of degraded broadcast channel with side information and confidential messages. This work is from Steinberg's work on the degraded broadcast channel with causal and noncausal side information, and Csisz$\acute{a}$r-Körner's work on broadcast channel with confidential messages. Inner and outer bounds on the capacity-equivocation regions are provided for the noncausal and causal cases. Superposition coding and double-binning technique are used in the corresponding achievability proofs. Then, we investigate the degraded broadcast channel with side information, confidential messages and noiseless feedback. The noiseless feedback is from the non-degraded receiver to the channel encoder. Inner and outer bounds on the capacity-equivocation region are provided for the noncausal case, and the capacity-equivocation region is determined for the causal case. Compared with the model without feedback, we find that the noiseless feedback helps to enlarge the inner bounds for both causal and noncausal cases. In the achievability proof of the feedback model, the noiseless feedback is used as a secret key shared by the non-degraded receiver and the transmitter, and therefore, the code construction for the feedback model is a combination of superposition coding, Gel'fand-Pinsker's binning, block Markov coding and Ahlswede-Cai's secret key on the feedback system.

cs.IT↗
arXiv2012

Regenerative and Adaptive schemes Based on Network Coding for Wireless Relay Network

Recent technological advances in wireless communications offer new opportunities and challenges for relay network.To enhance system performance, Demodulate-Network Coding (Dm-NC) scheme has been examined at relay node; it works directly to De-map the received signals and after that forward the mixture to the destination. Simulation analysis has been proven that the performance of Dm-NC has superiority over analog-NC. In addition, the Quantize-Decode-NC scheme (QDF-NC) has been introduced. The presented simulation results clearly provide that the QDF-NC perform better than analog-NC. The toggle between analogNC and QDF-NC is simulated in order to investigate delay and power consumption reduction at relay node.

cs.IT↗
arXiv2012

Relay selection for multiple access relay channel with decode-forward and analog network coding

This paper presents a relay selection for decode-and-forward based on network coding (DF-NC) and analog-NC protocols in general scheme of cellular network system. In the propose scheme the two source node simultaneously transmit their own information to all the relays as well as the destination node, and then, a single relay i.e. best with a minimum symbol error rate (SER) will be selected to forward the new version of the received signal. Simulation results show that, the DF-NC scheme with considerable performance has exactness over analog-NC scheme. To improve the system performance, optimal power allocation between the two sources and the best relay is determined based on the asymptotic SER. By increasing the number of relays node, the optimum power allocation achieve better performance than asymptotic SER.

cs.IT↗
arXiv2010

Penalized Likelihood Regression in Reproducing Kernel Hilbert Spaces with Randomized Covariate Data

Classical penalized likelihood regression problems deal with the case that the independent variables data are known exactly. In practice, however, it is common to observe data with incomplete covariate information. We are concerned with a fundamentally important case where some of the observations do not represent the exact covariate information, but only a probability distribution. In this case, the maximum penalized likelihood method can be still applied to estimating the regression function. We first show that the maximum penalized likelihood estimate exists under a mild condition. In the computation, we propose a dimension reduction technique to minimize the penalized likelihood and derive a GACV (Generalized Approximate Cross Validation) to choose the smoothing parameter. Our methods are extended to handle more complicated incomplete data problems, such as, covariate measurement error and partially missing covariates.

stat.ME↗