Methods and apparatuses are provided for reception path selection, including inputting, to a trained
machine learning module (MLM), path identifications representing paths selected as having a best reception quality for respective preceding reception periods; obtaining from the MLM, an identification of a predicted path predicted to provide a best reception quality in an upcoming reception period; and selecting the predicted path for receiving signals in the upcoming reception period. Moreover, methods and apparatuses are provided for determining a time period comprising: inputting, to a MLM, time period settings for the
receiver diversity configuration previously selected as best for respective predetermined time intervals, wherein the time period setting is a length of time period in which the
receiver diversity configuration remains the same; obtaining, from the MLM, a predicted time period for the upcoming time interval, predicted to be best among predefined time period settings according to a predefined criterion; and setting the predicted time period for the upcoming time interval. Moreover, methods and apparatuses for training the MLMs are provided.