Correlating Local Weather Conditions with Cellular Network Performance Indicators

Abstract: In this thesis, the relationships between local weather and the performance of a cellular telecommunications network were investigated by means of data analysis. Models of the average daily cycles in the data were developed accounting for cyclic behaviour and seasonal trends. Several different analysis methods were then performed on data measuring deviations from these average cycles. The methods used were multiple linear regression, partial least squares regression, calculation of Spearman's correlation coefficients, and regression by artificial neural networks. Some of the results indicate that the number of calls being attempted in the network is related to the local weather conditions. Additionally, small indications were found that the percentage of failed calls in the network was related to the amount of precipitation. These findings could potentially be valuable for network operators.