A machine learning enabled network planning tool

Abstract

In the coming years, planning future mobile networks will be infinitely more complex than nowadays. Future networks are expected to present multiple Network Management (NM) challenges to operators, such as managing network complexity in terms of densification of scenarios, heterogeneous nodes, applications, Radio Access Technologies (RAT), among others. In this context, the exploitation of past information gathered by the network is highly relevant when planning future deployments. In this paper we present a network planning tool based on Machine Learning (ML). In particular, we propose an approach which allows to predict Quality of Service (QoS) offered to end-users, based on data collected by the Minimization of Drive Tests (MDT) function. As a QoS indicator, we focus on Physical Resource Block (PRB) per Megabit (Mb) in an arbitrary point of the network. Minimizing this metric allows serving users with the same QoS by consuming less resources, and therefore, being more cost-effective. The proposed network planning tool considers a Genetic Algorithm (GA), which tries to reach the operator targets. The network parameters we desire to optimise are set as the input to the algorithm. Then, we predict the QoS of the network by means of ML techniques. By integrating these techniques in a network planning tool, operators would be able to find the most appropriate deployment layout, by minimizing the resources (i.e., the cost) they need to deploy to offer a given QoS in a newly planned deployment.