Hierarchical Performance Analysis on Random Linear Network Coding

Random linear network coding (RLNC) is a promising network coding solution when the network topology information is not fully available to all the nodes. However, in practice, nodes have partial knowledge of the network topology information. Motivated by this, we investigate the performance of RLNC...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on communications Ročník 66; číslo 5; s. 2009 - 2021
Hlavní autori: Li, Dan, Guang, Xuan, Zhou, Zhiheng, Li, Congduan, Tan, Chee Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
asymptotic behavior
Asymptotic properties
Coding
Decoding
Encoding
failure probability
hierarchical performance
Knowledge management
Network coding
Network topologies
Network topology
Nodes
Peer-to-peer computing
Performance analysis
Random linear network coding
Upper bound
Upper bounds
ISSN:0090-6778, 1558-0857
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Random linear network coding (RLNC) is a promising network coding solution when the network topology information is not fully available to all the nodes. However, in practice, nodes have partial knowledge of the network topology information. Motivated by this, we investigate the performance of RLNC and obtain different upper bounds on the failure probability of RLNC for the network constrained by different partial network topology information. These upper bounds not only improve the existing ones in the literature, but also show that the partial network topology information can bring benefits to the performance analysis of RLNC. On the other hand, it is observed that if more network topology information can be utilized, tighter upper bounds can be obtained, as expected. The upper bounds on two classical networks are compared for demonstration. To obtain a deeper understanding about the performance of RLNC, the asymptotic behavior of RLNC as the field size goes to infinity is also investigated.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2017.2787991