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Proceedings Paper

Models for discrete-time self-similar vector processes with application to network traffic
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Paper Abstract

The paper defines self-similarity for vector processes by employing the discrete-time continuous-dilation operation which has successfully been used previously by the authors to define 1-D discrete-time stochastic self-similar processes. To define self-similarity of vector processes, it is required to consider the cross-correlation functions between different 1-D processes as well as the autocorrelation function of each constituent 1-D process in it. System models to synthesize self-similar vector processes are constructed based on the definition. With these systems, it is possible to generate self-similar vector processes from white noise inputs. An important aspect of the proposed models is that they can be used to synthesize various types of self-similar vector processes by choosing proper parameters. Additionally, the paper presents evidence of vector self-similarity in two-channel wireless LAN data and applies the aforementioned systems to simulate the corresponding network traffic traces.

Paper Details

Date Published: 23 July 2003
PDF: 11 pages
Proc. SPIE 5100, Digital Wireless Communications V, (23 July 2003); doi: 10.1117/12.498577
Show Author Affiliations
Seungsin Lee, Rochester Institute of Technology (United States)
Raghuveer M. Rao, Rochester Institute of Technology (United States)
Rajesh Narasimha, Georgia Institute of Technology (United States)

Published in SPIE Proceedings Vol. 5100:
Digital Wireless Communications V
Raghuveer M. Rao; Soheil A. Dianat; Michael D. Zoltowski, Editor(s)

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