Robust interference identification for multi-RAT optimization in wireless cellular networks

Abstract

The objective of this study is to devise novel cognitive interference identification techniques for UMTS and LTE networks. We apply machine learning techniques to reconstruct interference patterns using a priori system knowledge, limited user information and sparse pathloss and interference measurements. The obtained interference patterns are used to build a multi-RAT optimization procedure aiming at energy efficient operation.