Exact Decoding Probability Under Random Linear Network Coding

In this letter, we compute the exact probability that a receiver obtains N linearly independent packets among K ≥ N received packets, when the sender/s use/s random linear network coding over a Galois Field of size q. Such condition maps to the receiver's capability to decode the original infor...

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Bibliographic Details
Published in:IEEE communications letters Vol. 15; no. 1; pp. 67 - 69
Main Authors: Trullols-Cruces, O, Barcelo-Ordinas, J M, Fiore, M
Format: Journal Article Publication
Language:English
Published: New York, NY IEEE 01.01.2011
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Applied sciences
Coding
Coding, codes
Computer networks
Computer Science
Computer simulation
Decoding
Encoding
Enginyeria de la telecomunicació
error control
Exact sciences and technology
Galois fields
Implementació
Information, signal and communications theory
Mathematical analysis
Mathematical models
Network coding
Networking and Internet Architecture
Networks
Random linear network coding
Receivers
Signal and communications theory
Telecommunications and information theory
Telemàtica i xarxes d'ordinadors
Upper bound
Vectors
Wireless communication systems
wireless networks
Xarxes sense fils
Àrees temàtiques de la UPC
Finite field
Upper bound
Linear coding
Grounding
Wireless telecommunication
error control
Wireless network
wireless networks
Decoding
Random coding
Random linear network coding
ISSN:1089-7798, 1558-2558
Online Access:Get full text
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Description
Summary:In this letter, we compute the exact probability that a receiver obtains N linearly independent packets among K ≥ N received packets, when the sender/s use/s random linear network coding over a Galois Field of size q. Such condition maps to the receiver's capability to decode the original information, and its mathematical characterization helps to design the coding so to guarantee the correctness of the transmission. Our formulation represents an improvement over the current upper bound for the decoding probability, and provides theoretical grounding to simulative results in the literature.
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2010.110310.101480