27768281
Video broadcasting over MIMO-OFDM systems
In this paper, we address the problem of video broadcasting over the orthogonal frequency division multiplexing (OFDM) systems with multiple antennas in both transmitter and receiver. In broadcast scenario, we propose to utilize the scalable video source with multiple description transcoding to provide multiple replicas for transmission, and post-processing is proposed to acquire parallel spatial subchannels. Our proposed approach minimizes the maximal performance degradation for video broadcasting users with power-efficient assignment of source descriptions to spatial subchannels. The proposed system efficiently combines progressive source coding with MIMO channels to implement robust video broadcasting over MIMO-OFDM systems. Simulation results demonstrate the advantages of our proposed approach.
206750751
Error robust scalable audio streaming over wireless IP networks
Streaming high-fidelity audio over wireless Internet protocol (IP) networks is a challenging task because the networks present not only packet losses, but also residual bit errors. These losses and errors have severe adverse effect on the compressed audio bitstream. To solve this problem, this paper introduces error resilience in conjunction with error protection for scalable audio streaming over wireless networks. Specifically, error resilience is achieved by performing bitstream data partitioning and reversible variable length coding in the audio coder. Error protection is provided by layered product channel code to simultaneously handle packet losses and residual bit errors. Both the row and column codes of the product code provide unequal error protection for different layers of the audio bitstream by considering the characteristics of the scalable audio. Rate-distortion optimization is performed to determine the best source-channel coding tradeoff that minimizes the average expected end-to-end distortion. Simulation results demonstrate the effectiveness of our proposed approach.
8668229
Bounding the power rate function of wireless ad hoc networks
Given a wireless ad hoc network and an end-to-end traffic pattern, the power rate function refers to the minimum total power required to support different throughput under a layered model of wireless networks. A critical notion of the layered model is the realizable graphs, which describe possible hit-rate supplies on the links by the physical and medium access layers. Under the layered model, the problem of finding the power rate function can he transformed into finding the minimum-power realizable graph that can provide a given throughput. We introduce a usage conflict graph to represent the conflicts among different uses of the wireless medium. Testing the realizability of a given graph can be transformed into finding the (vertex) chromatic number, i.e., the minimum number of colors required in a proper vertex-coloring, of the associated usage conflict graph. Based on an upper bound of the chromatic number, we propose a linear program that outputs an upper bound of the power rate function. A lower bound of the chromatic number is the clique number. We propose a systematic way of identifying cliques based on a geometric analysis of the space sharing among active links. This leads to another linear program, which yields a lower bound of the power rate function. We further apply greedy vertex-coloring to fine tune the bounds. Simulations results demonstrate that the obtained bounds are tight in the low power and low rate regime.
17722556
Network-Adaptive Cache Management Schemes for Mixed Media
For efficently caching mixed-media, this paper discusses several network-adaptive cache management schemes. Considering the characteristics of different types of media, we first proposed a multi-level page-size management scheme. Then, a media-characteristic-weighted replacement policy is presented to improve the hit ratio of mixed media including continuous and noncontinuous media. Lastly, a network-condition- and media-quality-adaptive resource management mechanism is described to dynamically re-allocate cache resource for different types of media according to their request patterns. Simulation results demonstrate effectiveness of our propsoed approaches.
1741788
A Compound TCP Approach for High-Speed and Long Distance Networks
Many applications require fast data transfer over high speed and long distance networks. However, standard TCP fails to fully utilize the network capacity due to the limitation in its conservative congestion control (CC) algorithm. Some works have been proposed to improve the connection 鈂s throughput by adopt- ing more aggressive loss-based CC algorithms. These algorithms, although can effectively improve the link utilization, have the weakness of poor RTT fairness. Further, they may severely de- crease the performance of regular TCP flows that traverse the same network path. On the other hand, pure delay-based ap- proaches that improve the throughput in high-speed networks may not work well when the traffic is mixed with both delay- based and greedy loss-based flows. In this paper, we propose a novel Compound TCP (CTCP) approach, which is a synergy of delay-based and loss-based approach. Specifically, we add a scal- able delay-based component into the standard TCP Reno conges- tion avoidance algorithm (a.k.a., the loss-based component). The sending rate of CTCP is controlled by both components. This new delay-based component can rapidly increase sending rate when network path is under utilized, but gracefully retreat in a busy network when bottleneck queue is built. Augmented with this delay-based component, CTCP provides very good bandwidth scalability with improved RTT fairness, and at the same time achieves good TCP-fairness, irrelevant to the windows size. We developed an analytical model of CTCP and implemented it on the Windows operating system. Our analysis and experiment results verify the properties of CTCP.
35872608
On the Performance Analysis of Network-Coded Cooperation in Wireless Networks
Cooperative communication is a powerful technology combating fading in wireless medium. In contrast to conventional cooperation (CC) which allows the relay to simply process and forward what it has heard, network-coded cooperation (NCC) endows network coding capability with the relay, i.e., allows the relay to first encode the data it has received from different sources and then forward the network-coded data to corresponding destinations. In this paper we systematically investigate the performance of NCC in terms of diversity-multiplexing tradeoff as well as exact system outage behavior. Under the assumption (denoted as A) that each destination can reliably overhear the data from other sources, we prove the diversity-multiplexing tradeoff of NCC outperforms that of CC. Moreover, we derive the close form of the system outage probability of NCC as a function of signal-to-noise ratio (SNR). The analytical and numerical results show that by requiring less bandwidth cost, NCC offers a similar or even reduced system outage probability compared with CC, and achieves the same full diversity order as CC at high SNR. Finally we discuss the performance of NCC in case the assumption A is removed. For a wireless network composed of N s-d pairs and a single relay, although there is a certain system outage probability increase for NCC, it still provides the same full diversity order of 2 as CC at high SNR, and has a diversity-multiplexing tradeoff superior to CC.
3992721
Error minimization in approximate range aggregates
Histogram techniques have been used in many commercial database management systems to estimate a query result size. Recently, it has been shown that they are very effective to support approximation of query processing especially aggregates. In this paper, we investigate the problem of minimizing average errors of approximate aggregates using histogram techniques. Firstly, we present a novel linear-spline histogram model that is more accurate than the existing models. Secondly, we propose a novel histogram construction technique for minimizing such average errors, which is shown to generate a near optimal histogram. Our experiment results demonstrate that the new histogram construction techniques lead to a great accuracy improvement on the existing techniques.
7426259
Modelling the Population Distribution in Multi-objective Optimization by Generative Topographic Mapping
Under mild conditions, the Pareto set of a continuous multi-objective optimization problem exhibits certain regularity. We have recently advocated taking into consideration such regularity in designing multi-objective evolutionary algorithms. Following our previous work on using Local Principal Component Analysis for capturing the regularity, this paper presents a new approach for acquiring and using the regularity of the Pareto set in evolutionary algorithms. The approach is based on the Generative Topographic Mapping and can be regarded as an Estimation of Distribution Algorithm. It builds models of the distribution of promising solutions based on regularity patterns extracted from the previous search, and samples new solutions from the models thus built. The proposed algorithm has been compared with two other state-of-the-art algorithms, NSGA-II and SPEA2 on a set of test problems.
2285434
Towards multidimensional subspace skyline analysis
The skyline operator is important for multicriteria decision-making applications. Although many recent studies developed efficient methods to compute skyline objects in a given space, none of them considers skylines in multiple subspaces simultaneously. More importantly, the fundamental problem on the semantics of skylines remains open: Why and in which subspaces is (or is not) an object in the skyline? Practically, users may also be interested in the skylines in any subspaces. Then, what is the relationship between the skylines in the subspaces and those in the super-spaces? How can we effectively analyze the subspace skylines? Can we efficiently compute skylines in various subspaces and answer various analytical queries?In this article, we tackle the problem of multidimensional subspace skyline computation and analysis. We explore skylines in subspaces. First, we propose the concept of Skycube, which consists of skylines of all possible nonempty subspaces of a given full space. Once a Skycube is materialized, any subspace skyline queries can be answered online. However, Skycube cannot fully address the semantic concerns and may contain redundant information. To tackle the problem, we introduce a novel notion of skyline group which essentially is a group of objects that coincide in the skylines of some subspaces. We identify the decisive subspaces that qualify skyline groups in the subspace skylines. The new notions concisely capture the semantics and the structures of skylines in various subspaces. Multidimensional roll-up and drill-down analysis is introduced. We also develop efficient algorithms to compute Skycube, skyline groups and their decisive subspaces. A systematic performance study using both real data sets and synthetic data sets is reported to evaluate our approach.
27094711
An approach to multiple attribute decision making based on fuzzy preference information on alternatives
This paper investigates the multiple attribute decision making problem with preference information on alternatives, in which multiple decision makers give their preference information in three forms, i.e., preference orderings, utility values and fuzzy preference relation. A new approach is presented to make use of both the decision makers' social fuzzy preference relation on alternatives and decision matrix to form an optimization model. The optimization model can be used to determine the attribute weights and rank the alternatives. The approach provides a new way to reflect the decision makers' social preference information and the decision matrix. Finally, an example is used to illustrate the proposed approach.
18316669
On Linear-Spline Based Histograms
Approximation is a very effective paradigm to speed up query processing in large databases. One popular approximation mechanism is data size reduction. There are three reduction techniques: sampling, histograms, and wavelets. Histogram techniques are supported by many commercial database systems, and have been shown very effective for approximately processing aggregation queries. In this paper, we will investigate the optimal models for building histograms based on linear spline techniques. We will firstly propose several novel models. Secondly, we will present efficient algorithms to achieve these proposed optimal models. Our experiment results showed that our new techniques can greatly improve the approximation accuracy comparing to the existing techniques.