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

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