10,675 research outputs found

    Final report on the evaluation of RRM/CRRM algorithms

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    Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin

    Cooperative medium access control based on spectrum leasing

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    Based on cooperative spectrum leasing, a distributed “win–win” (WW) cooperative framework is designed to encourage the licensed source node (SN) to lease some part of its spectral resources to the unlicensed relay node (RN) for the sake of simultaneously improving the SN’s achievable rate and for reducing the energy consumption (EC). The potential candidate RNs carry out autonomous decisions concerning whether to contend for a cooperative transmission opportunity, which could dissipate some of their battery power, while conveying their traffic in light of their individual service requirements. Furthermore, a WW cooperative medium-access-control (MAC) protocol is designed to implement the proposed distributed WW cooperative framework. Simulation results demonstrate that our WW cooperative MAC protocol is capable of providing both substantial rate improvements and considerable energy savings for the cooperative spectrum leasing system

    Design and Analysis of IPACT-based Bandwidth Allocation for Delay-Guarantee in OFDMA-PON

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    To guarantee delay performances for timesensitive services in an orthogonal frequency-division multiple access passive optical network (OFDMA-PON), we propose a two-dimension (i.e., subcarriers and time) upstream bandwidth allocation method based on interleaved polling with adaptive cycle time (IPACT). We first analyze its delay performance in terms of cycle time, i.e., the length of a polling cycle. Then, by setting the maximum polling cycle so as to guarantee timely transmissions for time-sensitive services, we identify the requirements, i.e., maximum bandwidth allocation, maximum number of allowed optical network units (ONUs), and optimum number of subcarriers, for upstream bandwidth allocation with delay guarantees. The proposed scheme is evaluated both numerically and via simulation

    Exploiting programmable architectures for WiFi/ZigBee inter-technology cooperation

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    The increasing complexity of wireless standards has shown that protocols cannot be designed once for all possible deployments, especially when unpredictable and mutating interference situations are present due to the coexistence of heterogeneous technologies. As such, flexibility and (re)programmability of wireless devices is crucial in the emerging scenarios of technology proliferation and unpredictable interference conditions. In this paper, we focus on the possibility to improve coexistence performance of WiFi and ZigBee networks by exploiting novel programmable architectures of wireless devices able to support run-time modifications of medium access operations. Differently from software-defined radio (SDR) platforms, in which every function is programmed from scratch, our programmable architectures are based on a clear decoupling between elementary commands (hard-coded into the devices) and programmable protocol logic (injected into the devices) according to which the commands execution is scheduled. Our contribution is two-fold: first, we designed and implemented a cross-technology time division multiple access (TDMA) scheme devised to provide a global synchronization signal and allocate alternating channel intervals to WiFi and ZigBee programmable nodes; second, we used the OMF control framework to define an interference detection and adaptation strategy that in principle could work in independent and autonomous networks. Experimental results prove the benefits of the envisioned solution

    DSRC Versus LTE-V2X: Empirical Performance Analysis of Direct Vehicular Communication Technologies

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    Vehicle-to-Vehicle (V2V) communication systems have an eminence potential to improve road safety and optimize traffic flow by broadcasting Basic Safety Messages (BSMs). Dedicated Short-Range Communication (DSRC) and LTE Vehicle-to-Everything (V2X) are two candidate technologies to enable V2V communication. DSRC relies on the IEEE 802.11p standard for its PHY and MAC layer while LTE-V2X is based on 3GPP’s Release 14 and operates in a distributed manner in the absence of cellular infrastructure. There has been considerable debate over the relative advantages and disadvantages of DSRC and LTE-V2X, aiming to answer the fundamental question of which technology is most effective in real-world scenarios for various road safety and traffic efficiency applications. In this paper, we present a comprehensive survey of these two technologies (i.e., DSRC and LTE-V2X) and related works. More specifically, we study the PHY and MAC layer of both technologies in the survey study and compare the PHY layer performance using a variety of field tests. First, we provide a summary of each technology and highlight the limitations of each in supporting V2X applications. Then, we examine their performance based on different metrics

    Second Workshop on Practical Use of Coloured Petri Nets and Design/CPN.

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    This report contains the proceedings of the Second Workshop on Practical Use of Coloured Petri Nets and Design/CPN, October 13-15, 1999. The workshop was organised by the CPN group at the Department of Computer Science at the University of Aarhus, Denmark. The individual papers are available in electronic form via the web pages: http://www.daimi.au.dk/CPnets/workshop99

    Estimating Dependency, Monitoring and Knowledge Discovery in High-Dimensional Data Streams

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    Data Mining – known as the process of extracting knowledge from massive data sets – leads to phenomenal impacts on our society, and now affects nearly every aspect of our lives: from the layout in our local grocery store, to the ads and product recommendations we receive, the availability of treatments for common diseases, the prevention of crime, or the efficiency of industrial production processes. However, Data Mining remains difficult when (1) data is high-dimensional, i.e., has many attributes, and when (2) data comes as a stream. Extracting knowledge from high-dimensional data streams is impractical because one must cope with two orthogonal sets of challenges. On the one hand, the effects of the so-called "curse of dimensionality" bog down the performance of statistical methods and yield to increasingly complex Data Mining problems. On the other hand, the statistical properties of data streams may evolve in unexpected ways, a phenomenon known in the community as "concept drift". Thus, one needs to update their knowledge about data over time, i.e., to monitor the stream. While previous work addresses high-dimensional data sets and data streams to some extent, the intersection of both has received much less attention. Nevertheless, extracting knowledge in this setting is advantageous for many industrial applications: identifying patterns from high-dimensional data streams in real-time may lead to larger production volumes, or reduce operational costs. The goal of this dissertation is to bridge this gap. We first focus on dependency estimation, a fundamental task of Data Mining. Typically, one estimates dependency by quantifying the strength of statistical relationships. We identify the requirements for dependency estimation in high-dimensional data streams and propose a new estimation framework, Monte Carlo Dependency Estimation (MCDE), that fulfils them all. We show that MCDE leads to efficient dependency monitoring. Then, we generalise the task of monitoring by introducing the Scaling Multi-Armed Bandit (S-MAB) algorithms, extending the Multi-Armed Bandit (MAB) model. We show that our algorithms can efficiently monitor statistics by leveraging user-specific criteria. Finally, we describe applications of our contributions to Knowledge Discovery. We propose an algorithm, Streaming Greedy Maximum Random Deviation (SGMRD), which exploits our new methods to extract patterns, e.g., outliers, in high-dimensional data streams. Also, we present a new approach, that we name kj-Nearest Neighbours (kj-NN), to detect outlying documents within massive text corpora. We support our algorithmic contributions with theoretical guarantees, as well as extensive experiments against both synthetic and real-world data. We demonstrate the benefits of our methods against real-world use cases. Overall, this dissertation establishes fundamental tools for Knowledge Discovery in high-dimensional data streams, which help with many applications in the industry, e.g., anomaly detection, or predictive maintenance. To facilitate the application of our results and future research, we publicly release our implementations, experiments, and benchmark data via open-source platforms
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