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Permanent link (DOI): https://doi.org/10.7939/R3X05XS4J

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Random Linear Network Coding for Non-Multicast and Multi-Resolution Multicast Problems Open Access

Descriptions

Other title
Subject/Keyword
network coding
subspace coding
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Karimian, Pourya
Supervisor and department
Ardakani, Masoud (Electrical and Computer Engineering)
Examining committee member and department
Tellambura, Chintha (Electrical and Computer Engineering)
Khabbazian, Majid (Electrical and Computer Engineering)
Ardakani, Masoud (Electrical and Computer Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Communications
Date accepted
2017-06-09T15:13:10Z
Graduation date
2017-11:Fall 2017
Degree
Master of Science
Degree level
Master's
Abstract
In this dissertation, we study two network coding problems. First, we consider a class of networks that we call funnel networks. In this class of networks the total capacity of the incoming links to each intermediate node is not less than the total capacity of its outgoing links. We then prove that any feasible non-multicast problem on funnel networks is solvable by routing. This proves that a linear network coding solution exist for any non-multicast problem on funnel networks. The desirability of network coding in funnel networks may be justified by the other benefits that coding offers. However, we see that in funnel networks, the conventional random approach to linear coding fails with high probability. Hence, we provide a new random linear network coding solution for these non-multicast problems. Second, we study multicast problems in arithmetic network coding (ANC) in which, finite field arithmetic operations are replaced by real or complex arithmetic operations. A major issue in random ANC is that the condition number of the network grows quickly with the network size, hence, small errors in links can cause substantial decoding mistakes at sinks. We propose a new encoding method based on subspace coding along with a rank deficient decoding method. Our simulation results show significant improvements over conventional ANC.
Language
English
DOI
doi:10.7939/R3X05XS4J
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
Karimian P, Borujeny RR, Ardakani M. On network coding for funnel networks. IEEE Communications Letters. 2015 Nov;19(11):1897-900.Karimian P, Ardakani M. Rank deficient decoding for arithmetic subspace network coding. InStudents on Applied Engineering (ISCAE), International Conference for 2016 Oct 20 (pp. 402-406). IEEE.

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