Random Matrix Theory Rate Learning for Cognitive Small Cells

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