CELL OUTAGE DETECTION IN LTE-A CELLULAR SYSTEMS USING NEURAL NETWORKS

Year 2018, Volume: 60 Issue: 1, 31 - 40, 31.07.2018

Hasan Tahsin Oğuz Aykut Kalaycıoğlu

Abstract

Self-organizing networks (SONs) are considered as one of the key features for automation of network management in new generation of mobile communications. The upcoming fifth generation (5G) mobile networks are likely to offer new challenges for SON solutions. In SON structure, self-healing is an outstanding task which comes along with Cell Outage Detection (COD) and Cell Outage Compensation (COC). This study investigates the detection of cell outages by means of the metrics generated in the User Equipment (UE) with the help of pattern recognition methods such as Neural Networks, Logistic Regression and k-Means algorithms. Based on the metrics like Signal to Interference Noise Ratio (SINR), Reference Signal Received Quality (RSRQ), and Channel Quality Indicator (CQI), large amount of data is processed with supervised and unsupervised algorithms for the purpose of classifying outages and possible degradations. Our results suggest that in 79.74% of the simulation cases, Neural Network structure was able to identify the correct state of the cells whether it is outage or not with a true positive rate of 87.61% and a true negative rate of 71.87% whereas Logistic Regression gave a success rate of 78.73%, true positive rate of 88.15%, and true negative rate of 69.3%. As a future work, more sophisticated state-of-the-art deep learning mechanisms can lead us to much more successful results in cell outage detection.   

Keywords

Cell outage detection, neural networks, 5G, self-organizing networks

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