CIVILICA We Respect the Science
Publisher of Iranian Journals and Conference Proceedings
Paper
title

A Markov Chain Model for Sparse Network Coding

Credit to Download: 1 | Page Numbers 8 | Abstract Views: 55
Year: 2019
COI code: ISCELEC03_083
Paper Language: English

How to Download This Paper

For Downloading the Fulltext of CIVILICA papers please visit the orginal Persian Section of website.

Authors A Markov Chain Model for Sparse Network Coding

  Amir Zarei - Department of Computer Science and Information, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
  Peyman Pahlevani - Department of Computer Science and Information, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran

Abstract:

Random Linear Network Coding (RLNC) has been demonstrated to provide an efficient communication scheme, leveraging a considerable stability against packet losses in error-prone network. However, it suffers from a high computational complexity and some novel approaches have been recently proposed. One of such solutions is Tunable Sparse Network Coding (TSNC), where merely few original packets are mixed in each transmission. The number of original packets to be mixed in each transmission can be chosen from a density parameter/distribution, which could be eventually adapted. In this article, we present a complete analytical model that describes the performance of SNC on a precise way. We exploit an absorbing Markov model where the states are defined by the number of transmitted coded packets received by the decoder, and the number of non-zero columns at decoding matrix. The model is validated by use of a simulation campaign, and the difference between model and simulation is negligible. The proposed model would enable a more accurate evaluation of the behavior of SNC techniques for large finite field size.

Keywords:

Random Linear Network Coding – Sparse Network Coding- Tunable Sparse Network Coding

Perma Link

https://www.civilica.com/Paper-ISCELEC03-ISCELEC03_083.html
COI code: ISCELEC03_083

how to cite to this paper:

If you want to refer to this article in your research, you can easily use the following in the resources and references section:
Zarei, Amir & Peyman Pahlevani, 2019, A Markov Chain Model for Sparse Network Coding, Third National Conference on Electrical and Computer Engineering, تهران, دبيرخانه دائمي كنفرانس, https://www.civilica.com/Paper-ISCELEC03-ISCELEC03_083.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Zarei, Amir & Peyman Pahlevani, 2019)
Second and more: (Zarei & Pahlevani, 2019)
For a complete overview of how to citation please review the following CIVILICA Guide (Citation)

Scientometrics

The University/Research Center Information:
Type: state university
Paper No.: 331
in University Ranking and Scientometrics the Iranian universities and research centers are evaluated based on scientific papers.

Research Info Management

Export Citation info of this paper to research management softwares

New Related Papers

Iran Scientific Advertisment Netword

Share this paper

WHAT IS COI?

COI is a national code dedicated to all Iranian Conference and Journal Papers. the COI of each paper can be verified online.