A PCA-based Approach to Modeling Location Update and Paging for Location Area Planning

In cellular networks, accurate predictions of location update and paging are essential to attain reliable results of location area planning. In this paper, a novel approach using principal component analysis is proposed to model location update and paging, revealing the low intrinsic dimensionality. Compared with existing methods, the proposed approach not only makes predictions adaptively, but also provides higher temporal resolution. These are valuable for dynamically adapting LAs to variation of location update and paging. The analysis is based on original data from a real cellular network.