Get Analytical and Stochastic Modeling Techniques and PDF

By Ulf Jensen, Armin Heindl (auth.), Khalid Al-Begain, Dieter Fiems, Gábor Horváth (eds.)

ISBN-10: 3642022057

ISBN-13: 9783642022050

This ebook constitutes the refereed lawsuits of the sixteenth foreign convention on Analytical and Stochastic Modeling suggestions and functions, ASMTA 2009, held in Madrid, Spain, in June 2009 along side ECMS 2009, the 23nd ecu convention on Modeling and Simulation.

The 27 revised complete papers provided have been conscientiously reviewed and chosen from fifty five submissions. The papers are geared up in topical sections on telecommunication networks; instant & cellular networks; simulation; quueing platforms & distributions; queueing & scheduling in telecommunication networks; version checking & technique algebra; functionality & reliability research of assorted systems.

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Read or Download Analytical and Stochastic Modeling Techniques and Applications: 16th International Conference, ASMTA 2009, Madrid, Spain, June 9-12, 2009. Proceedings PDF

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Extra info for Analytical and Stochastic Modeling Techniques and Applications: 16th International Conference, ASMTA 2009, Madrid, Spain, June 9-12, 2009. Proceedings

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As a matter of fact, this travel back methodology has been traditionally used in ACO [13]. 4 Experimental Results This section presents a performance evaluation of our proposed ant colony based IRC system. Specifically, we are interested in two aforementioned questions. Firstly, how much effectively does the ant based system switch between paths in comparison to a commercial IRC system? Secondly, what are the underlying performance criterions by which ants switch between paths? 1 Simulation Setup Our simulations are performed with a customized simulator, implemented in C++, which precisely model the network topology and traffic at packet level.

Once these probabilities are obtained, some performance metrics can be calculated. Note that, the following algorithm converges as long as λ1 + λ2 < c1 (µ1 + µ2 ). 1. 2. 3. 4. 5. 6. Set a certain convergence threshold κ. Input: c1 , λ1 , λ2 , µ1 and µ2 . Initialize pold i,j = 1 for i = j = 0 and pi,j = 0 for i + j > 0. Compute the probabilities pnew i,j using (1). new old new If pi,j − pi,j > κ, then set pold i,j = pi,j and go to Step 4. Once the steady-state probabilities pi,j are obtained, the blocking and dropping probabilities for SUs can be calculated Analysis of Opportunistic Spectrum Access in Cognitive Ad Hoc Networks 21 Performance Metrics.

Arrival rate λ1 Firstly, we show the impact of the number of secondary channels c2 and the variation of the arrival rate λ1 on the blocking and dropping probabilities of the SUs. 41 SU/sec. As shown in Figure 3, the blocking probability for the SU in both schemes increases with respect to the arrival rate of PU; λ1 . This can be explained as follows: As λ1 increases, the number of available channels that can be accessed opportunistically by the SUs reduces, which will lead to higher blocking probability for SUs.

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Analytical and Stochastic Modeling Techniques and Applications: 16th International Conference, ASMTA 2009, Madrid, Spain, June 9-12, 2009. Proceedings by Ulf Jensen, Armin Heindl (auth.), Khalid Al-Begain, Dieter Fiems, Gábor Horváth (eds.)


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