Abstract: A computer implemented method of predicting a rating score of a cellular service for a plurality of cellular subscribers, comprising obtaining one or more machine learning models trained with a plurality of feature vectors created for a subset of a plurality of cellular subscribers of a cellular operator participating in a survey to rate a cellular service provided by the cellular operator through a cellular network where each of the feature vectors is created by extracting a plurality of features of a respective cellular subscriber of the subset, each feature vector is associated with a rating score assigned by the respective cellular subscriber, predicting an estimated rating score of the cellular service for other cellular subscribers by applying the machine learning model(s) to the feature vector of extracted features of each of the other cellular subscribers and outputting the estimated rating score for each of the other cellular subscribers.

Abstract: According to some embodiments of the invention there is provided a method for evaluating a performance of a cellular radio network. The method comprises receiving two or more call detail records from a repository of a cellular radio network, wherein the cellular radio network comprises two or more directional sector antennas. The method comprises identifying two or more antenna pairs among the directional sector antennas, each one of the antenna pairs used to perform one of multiple subscriber calls documented in the call detail records. The method comprises calculating two or more sector pair usage parameters one for each of the antenna pairs according to an analysis of respective the subscriber calls. The method comprises analyzing the sector pair usage parameters to evaluate a performance of the cellular radio network.