A Fuzzy Inference Model for Evaluating Data Transfer in LTE Mobile Networks via Crowdsourced Data

Abstract

Improving the Quality of Service (QoS) in the data transfer of 4G Long-Term Evolution (LTE) mobile networks has been a significant concern. Previous analyses have focused on enhancing network infrastructure using statistical tools, computational algorithms, and fuzzy models to improve mobile network operators. Those works are based on simulated data or data collected by a specialized modem without providing user information. In this study, we propose a fuzzy inference model to evaluate QoS Key Performance Indicators and signal parameters using data acquired by user equipment through collaboration or crowdsourcing. This fuzzy inference model provides specialists with a new method for assessing the QoS and offers users relevant information on the quality of data transfer service in LTE networks. The evaluation is based on fuzzy QoS, and effectiveness indices are classified into five levels: Very Poor, Poor, Acceptable, Good, and Very Good. Furthermore, the model can evaluate other data samples different from those used in this proposal. Finally, this method can assess the data transfer of 5G networks, making respective adaptations.