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https://doi.org/10.1109/isit.2...
Article . 2011 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2011
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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SUperposition Noisy Network Coding

Authors: Neevan Ramalingam; Zhengdao Wang;

SUperposition Noisy Network Coding

Abstract

We present a superposition coding scheme for communication over a network, which combines partial decode and forward and noisy network coding. This hybrid scheme is termed as superposition noisy network coding. The scheme is designed and analyzed for single relay channel, single source multicast network and multiple source multicast network. The achievable rate region is determined for each case. The special cases of Gaussian single relay channel and two way relay channel are analyzed for superposition noisy network coding. The achievable rate of the proposed scheme is higher than the existing schemes of noisy network coding and compress-forward.

5 pages, 3 figures, conference

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Keywords

FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
Green