On the Partial Decoding Delay of Sparse Network Coding

Sparse network coding (SNC) is a promising technique for reducing the complexity of random linear network coding (RLNC), by selecting a sparse coefficient matrix to code the packets. However, the performance of SNC for the average decoding delay (ADD) of the packets is still unknown. We study the pe...

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Vydané v:IEEE communications letters Ročník 22; číslo 8; s. 1668 - 1671
Hlavní autori: Zarei, Amir, Pahlevani, Peyman, Davoodi, Mansoor
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Analytical models
Coding
Decoding
Delay
Delays
Encoding
Lower bounds
Markov chain model
Markov chains
Markov processes
Matrix methods
Network coding
Random linear network coding
Receivers
sparse network coding
Sparsity
ISSN:1089-7798, 1558-2558
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Popis
Shrnutí:Sparse network coding (SNC) is a promising technique for reducing the complexity of random linear network coding (RLNC), by selecting a sparse coefficient matrix to code the packets. However, the performance of SNC for the average decoding delay (ADD) of the packets is still unknown. We study the performance of ADD and propose a Markov chain model to analyze this SNC metric. This model provides a lower bound for decoding delay of a generation as well as a lower bound for decoding delay of a portion of a generation. Results show that although RLNC provides a better decoding delay of an entire generation, SNC outperforms RLNC in terms of ADD per packet. Sparsity of the coefficient matrix is a key parameter for ADD per packet to transmit stream data. The proposed model enables us to select the appropriate degree of sparsity based on the required ADD. Numerical results validate that the proposed model would enable a precise evaluation of SNC technique behavior.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2018.2840139