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.
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.
39711298
Data-Driven Feature Control Models for Creating Virtual Faces
This paper presents a novel data-driven method for creating realistic face models based on estimated high-level feature control models. Our method takes as examples 3D face scans. By bringing scanned models into full correspondence with a model fitting approach, we apply principal component analysis (PCA) to the exemplar shapes of each facial feature to build a shape space. We compute a set of face anthropometric measurements to parameterize example feature shapes in the measurement spaces. Using the PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions that are devoted to the generation of desired shape by taking the anthropometric parameters as input. The correspondence among all exemplar textures is obtained by parameterizing a 3D generic mesh over a 2D image domain. The new feature texture with desired attributes is synthesized by interpolating the example textures.
2899555
Scalable Video Transmission over the IEEE 802.11e Networks Using Cross-Layer Rate Control
This paper presents a novel cross-layer rate control scheme for optimizing 3D wavelet scalable video transmission over the IEEE 802.11e wireless local area networks. The proposed scheme consists of a macro and a micro rate control schemes residing at the application layer and the network sublayer respectively. The macro rate control uses bandwidth estimation to achieve optimal bit allocation with minimum distortion. The micro rate control employs an adaptive mapping of packets using video classifications. This prioritizes appropriately the video traffic to maximize the transmission protection to the important video packets. The performance is investigated by simulations showing advantages of our cross-layer design.
8620934
Certifying Concurrent Programs Using Transactional Memory
Transactional memory (TM) is a new promising concurrency-control mechanism that can avoid many of the pitfalls of the traditional lock-based techniques. TM systems handle data races between threads automatically so that programmers do not have to reason about the interaction of threads manually. TM provides a programming model that may make the development of multi-threaded programs easier. Much work has been done to explore the various implementation strategies of TM systems and to achieve better performance, but little has been done on how to formally reason about programs using TM and how to make sure that such reasoning is sound. In this paper, we focus on the semantics of transactional memory and present a proof-carrying code (PCC) system for reasoning about programs using TM. We formalize our reasoning with respect to the TM semantics, prove its soundness, and use examples to demonstrate its effectiveness.
2479481
Gene Regulatory Network Construction Using Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM)
Discovering gene relationship from gene expression data is a hot topic in the post-genomic era. In recent years, Bayesian network has become a popular method to reconstruct the gene regulatory network due to the statistical nature. However, it is not suitable for analyzing the time-series data and cannot deal with cycles in the gene regulatory network. In this paper we apply the dynamic Bayesian network to model the gene relationship in order to overcome these difficulties. By incorporating the structural expectation maximization algorithm into the dynamic Bayesian network model, we develop a new method to learn the regulatory network from the S. Cerevisiae cell cycle gene expression data. The experimental results demonstrate that. the accuracy of our method outperforms the previous work.
1781908
An EM-Based Time-Domain Channel Estimation Algorithm Using a priori Information
Based on the expectation-maximization (EM) algorithm, an iterative time-domain channel estimation approach capable of using a priori information is proposed for orthogonal frequency division multiplexing (OFDM) systems in this letter: it outperforms its noniterative counterpart in terms of estimation accuracy as well as bit error rate (BER) performance. Numerical simulations demonstrate that an SNR gain of 1 dB at BER = 10 -4 with only one iteration and estimation mean square error (MSE) which nearly coincides with the Cramer-Rao bound (CRB) in the low SNR region can be obtained, thanks to the efficient use of a priori information.