Skip to Main content Skip to Navigation
Conference papers

Random Matrix Theory Rate Learning for Cognitive Small Cells

Abstract : This paper addresses the problem of maximum data rate learning in small cells networks. Considering a shared carrier deployment, small cell users have to adapt their energy in such a way to not disturb macro-cellular communications. In such a context, small cell users would probably undergo unacceptable levels of interference, thereby considerably affecting their performance. The objective of our work is to propose a method for fast prediction of these events and their corresponding maximum achievable data rates. This can help small cell users to select the optimal transmission strategy.
Document type :
Conference papers
Complete list of metadata

Cited literature [10 references]  Display  Hide  Download
Contributor : Ejder Bastug Connect in order to contact the contributor
Submitted on : Tuesday, January 8, 2013 - 11:44:29 AM
Last modification on : Monday, August 30, 2021 - 9:40:02 AM
Long-term archiving on: : Tuesday, April 9, 2013 - 3:52:07 AM


Files produced by the author(s)


  • HAL Id : hal-00771250, version 1


Abla Kammoun, Romain Couillet, Mérouane Debbah. Random Matrix Theory Rate Learning for Cognitive Small Cells. WWRF 2012, Oct 2012, Berlin, Germany. 4p. ⟨hal-00771250⟩



Les métriques sont temporairement indisponibles