A Markov Chain Model for Sparse Network Coding

سال انتشار: 1398
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 410

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شناسه ملی سند علمی:

ISCELEC03_083

تاریخ نمایه سازی: 14 فروردین 1399

چکیده مقاله:

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.

کلیدواژه ها:

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

نویسندگان

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