Data analytics for forecasting cell congestion on LTE networks

• Author(s): Torres, Pedro ; Marques, Paulo ; Marques, Hugo ; Dionísio, Rogério Pais ; Alves, Tiago Ferreira ; Pereira, Luis Miguel Cardoso ; Ribeiro, Jorge Miguel Afonso
• Date: 2017
• Persistent ID: http://hdl.handle.net/10400.11/6076
• Origin: Repositório Científico do Instituto Politécnico de Castelo Branco
• Subject(s): LTE; SON; Machine Learning; Forecasting
• Document Type: Conference object
• Language: English
• Contributor(s): Repositório Científico do Instituto Politécnico de Castelo Branco

Description

This paper presents a methodology for forecasting the average downlink throughput for an LTE cell by using real measurement data collected by multiple LTE probes. The approach uses data analytics techniques, namely forecasting algorithms to anticipate cell congestion events which can then be used by Self-Organizing Network (SON) strategies for triggering network re-configurations, such as shifting coverage and capacity to areas where they are most needed, before subscribers have been impacted by dropped calls or reduced data speeds. The presented implementation results show the prediction of network behaviour is possible with a high level of accuracy, effectively allowing SON strategies to be enforced in time.