Recognition of Low-Dimensional Patterns in Radio Access Network Data

In this work, we aim at finding the low dimensional hidden structures or manifolds that exist in the high dimensional data produced by a Radio Access Network (RAN). Specifically, we consider the Key Performance Indicators (KPIs) of a UMTS network. The KPI data is obtained by performing semi-dynamic simulations of a Radio Network Planning (RNP) tool. The low-dimensional manifold, yielding a meaningful and tractable representation of the performance indicators, facilitates the complicated tasks like monitoring, troubleshooting, fault detection, design, radio resource management etc. We have applied one second-order linear (PCA), one high-order linear (ICA) and one nonlinear technique (ISOMAP) of manifold learning and compared the results.