Data-driven configuration recommendation for microwave networks A comparison of machine learning approaches for the recommendation of configurations and the detection of configuration anomalies

Abstract

As mobile networks grow and the demand for faster connections and a better reachability increases, telecommunication providers are looking ahead to an increasing effort to maintain and plan their networks. It is therefore of interest to avoid manual maintenance and planning of mobile networks and look into possibilities to help automate such processes. The planning and configuration of microwave link networks involves manual steps resulting in an increased effort for maintenance and the risk of manual mistakes. We therefore investigate the usage of the network’s data to train machine learning models that predict a link’s configuration setting for given information of its surroundings, and to give configuration recommendations for possible misconfigurations. The results show that the available data for microwave networks can be used to predict some configurations quite accurately and therefore presents an opportunity to automate parts of the configuration process for microwave links. However, the evaluation of our recommendations is challenging as the application of our recommendations is risky and might harm the networks in an early stage.