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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE communications letters Vol. 22; no. 8; pp. 1668 - 1671
Main Authors: Zarei, Amir, Pahlevani, Peyman, Davoodi, Mansoor
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2018.2840139