loading page

Scenario-agnostic localisation system for celullar network based on feature engineering
  • +4
  • Hao Qiang Luo Chen ,
  • Emil J. Kathib ,
  • Deepak Sethi ,
  • Eduardo Cruz ,
  • Asier Arostegui ,
  • Raúl Martín Cuerdo ,
  • Raquel Barco
Hao Qiang Luo Chen
University of Malaga

Corresponding Author:[email protected]

Author Profile
Emil J. Kathib
Author Profile
Deepak Sethi
Author Profile
Eduardo Cruz
Author Profile
Asier Arostegui
Author Profile
Raúl Martín Cuerdo
Author Profile
Raquel Barco
Author Profile

Abstract

In the last years, location-aware services and network management have driven the demand for user location estimation in mobile networks. Nevertheless, the location obtained from user terminals is not usually accessible to mobile operators. In addition, available cell Key Performance Indicators (KPI) vary highly from network to network, and only a few of them are always enabled widely. Currently prevalent Machine Learning (ML) based solutions have achieved high precisions, but they are bounded to a trained scenario, restricting their application to new areas. We propose a method for creating scenario-agnostic prediction models which solves the above problems by applying feature engineering, over a small set of easily obtainable KPIs, applicable for any ML method. Finally, the performance of the proposed method is demonstrated using a real network dataset.