Analysis of User Mobility Statistics for Cellular Network Re-Structuring

Abstract

Mobile network operators often use handover statistics to improve the structure of their networks. However, such statistics only reflect the movement of connected users. In this paper, a thorough investigation of the correlation between idle and connected user mobility statistics is performed based on measurements from a live GSM network. A multiple regression model is proposed to estimate the number of location updates due to mobility and non-mobility reasons on a per-cell basis from measurements in the network management system. Results shown that, although the number of location updates can normally be predicted accurately from handover statistics, especially when aggregated over large geographical areas, large deviations are observed in cells of lower layers in multi-tier networks.

Statistical learning and multiple linear regression model for network selection using MIH

“…Such statistics only reflect the movement of connected users. Toril et al [5] supplied a novel multiple regression model for GSM networks to estimate the number of location updates by idle users. Tiwana et al [6] used a regression analysis as statistical learning approach to propose a new model for handover.…”

“…For instance, a value of R 2 = 0.75 means that 75% of the choice concerning the selected network during handover process can be justified by the proposed model in (1). R 2 is used as a goodness-of-fit measure for regression analysis and it is calculated by the following formula: (5) Where N is the number of collected voice data record (9600 observations), Y is the mean value of the dependent variable Y (i.e. Network selected), Y i and are the measured and estimated values of Y.…”

Savings in Location Management Costs Leveraging User Statistics

“…The employment of the call and mobility patterns of the user can help optimize the location area's dimensions and minimize signaling costs [11]. In fact, mobile network operators often leverage handover statistics to improve the structure of their networks, with a strong impact on service performance and signaling load [12]. In this sense, user statistics-based algorithms for Location Management have proved to significantly reduce signaling costs [13][14][15].…”