Patent ID: 11943640
Assignee: TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)
Field: Computer technology (Electrical engineering)
Classification: CPC H  G | IPC G  H

Claim 19:
20. A method for training a generative machine learning model that outputs synthetic data as an input for a machine learning process that recommends radio access network (RAN) configurations, wherein the generative machine learning model is trained together with a discriminative machine learning model as adversaries, the method comprising:
obtaining a noise input;
obtaining non-synthetic data associating configuration management (CM) parameter values, RAN characteristic parameter values, and performance indicator values, wherein each performance indicator value indicates a performance for a given RAN configuration as defined by one or more of the non-synthetic CM parameter values and a given RAN characteristic as defined by one or more of the non-synthetic RAN characteristic parameter values;
generating, using the generative machine learning model, synthetic data from the noise input, wherein the synthetic data, in the same form as the non-synthetic data, comprises at least one of one or more synthetic CM parameter values, one or more synthetic RAN characteristic parameter values and one or more synthetic performance indicator values;
obtaining an input of either the synthetic data or the non-synthetic data and a corresponding true class;
classifying the input, using the discriminative machine learning model, into a first predicted class of either a class of synthetic data or a class of non-synthetic data;
updating the discriminative machine learning model by minimizing an error based on a deviation between the first predicted class and the true class; and
updating the generative machine learning model by maximizing the error of the discriminative machine learning model.