User mobility inference and clustering through LTE PDCCH data analysis

Abstract

The high penetration of mobile services provides an ample set of data generated by users and network elements. The analysis of such data yields insights on the behaviour of users and their experienced quality, and can be used by mobile operators to improve their mobile networks. In this paper, we design methods to infer user mobility patterns, estimate their channel quality and cluster them based on their Modulation and Coding Scheme (MCS) time evolution. In detail, we propose: i) a mapping between MCS and SNR, useful to assess the quality of the transmissions, ii) a method for deriving users’ approximate velocities and categorising user mobility, and iii) a hierarchical clustering algorithm using Dynamic Time Warping, able to generate meaningful user clusters according to communication length and quality. We apply those proposed methods to real traces collected from more than one week of observations of three operative base stations in Spain. We observe that our solutions successfully provide relevant information about users’ mobility and their channel quality, making them suitable for improvements in the understanding and planning of LTE resources by mobile network operators.