Machine Learning Assisted Selection Combining

A method for training a machine learning model for predicting a path to provide a best reception quality in an upcoming reception period among a plurality of possible reception paths, comprising:

• obtaining, for each of a first plurality of reception periods consecutive in time, an identification of a reception path that provides a best reception quality among a plurality of reception paths for the reception period based on reception quality measurement,
• inputting, to a machine learning model, for each of the first plurality of reception periods consecutive in time the obtained identification, and
• training the machine learning model to minimize the prediction error in a current reception period between:
• the obtained identification of a reception path that provides the best reception quality in the current reception period, and
• a predicted identification of a reception path that provides the best reception quality in the current reception period, wherein the predicted identification is predicted by the machine learning model based on the obtained identifications for a second plurality of reception periods preceding the current reception period.

A method for training a machine learning model for determining a time period for a receiver diversity configuration, comprising:

• inputting, to a machine learning model, time period settings for the receiver diversity configuration that were previously selected as best for respective predetermined time intervals, wherein the time period setting is a length of a time period in which the receiver diversity configuration remains the same, and
• training the machine learning model to minimize the prediction error in a current reception period between:
• a predicted time period for the upcoming predetermined time interval, predicted to be best among a plurality of predefined time period settings according to a predefined criterion, and
• a computed time period for the upcoming predetermined time interval, computed as sufficient for achieving desired reception quality.

An apparatus for reception path selection, comprising:

• processing circuitry configured to:
• input, to a trained machine learning model, a plurality of path identifications representing paths selected among a plurality of possible reception paths as having a best reception quality for respective preceding reception periods,
• obtain, as an output of the machine learning model, an identification of a predicted path that is predicted to provide a best reception quality in an upcoming reception period among the plurality of possible reception paths, and
• select the predicted path for receiving signals in the upcoming reception period.

An apparatus for determining a time period for a receiver diversity configuration, the apparatus comprising a receiver and processing circuitry configured to:

• input, to a machine learning model, time period settings for the receiver diversity configuration that were previously selected as best for respective predetermined time intervals, wherein the time period setting is a length of a time period in which the receiver diversity configuration remains the same,
• obtain, from an output of the machine learning model, a predicted time period for the upcoming predetermined time interval, predicted to be best among a plurality of predefined time period settings according to a predefined criterion,
• set the predicted time period for the receiver diversity configuration for the upcoming predetermined time interval, and
• control the receiver to receive the signals according to the set predicted time period.