Sparse recovery using sparse sensing matrix based finite field optimization in network coding

Ganzorig Gankhuyag; Eungi Hong; Yoonsik Choe

doi:10.1587/transinf.2016EDL8189

Sparse recovery using sparse sensing matrix based finite field optimization in network coding

Ganzorig Gankhuyag, Eungi Hong, Yoonsik Choe

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

Network coding (NC) is considered a new paradigm for distributed networks. However, NC has an all-or-nothing property. In this paper, we propose a sparse recovery approach using sparse sensing matrix to solve the NC all-or-nothing problem over a finite field. The effectiveness of the proposed approach is evaluated based on a sensor network.

Original language	English
Pages (from-to)	375-378
Number of pages	4
Journal	IEICE Transactions on Information and Systems
Volume	E100D
Issue number	2
DOIs	https://doi.org/10.1587/transinf.2016EDL8189
Publication status	Published - 2017 Feb

Bibliographical note

Publisher Copyright:
© 2017 The Institute of Electronics, Information and Communication Engineers.

All Science Journal Classification (ASJC) codes

Software
Hardware and Architecture
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Artificial Intelligence

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10.1587/transinf.2016EDL8189

Cite this

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Sparse recovery using sparse sensing matrix based finite field optimization in network coding

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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