Machine learning based tuning of radio frequency apparatuses

A system, comprising:

• a radio frequency apparatus configured to operate based on a plurality of possible configuration settings to generate an output signal that is characterized by a performance metric;
• a tuner that employs a machine learning engine having a training stage and an inference stage, wherein the inference stage is configured to, based on a machine learning model, search the possible configuration settings for a target configuration setting that results in the performance metric meeting defined bounds of an optimization threshold value;
• and
• a tester that determines the performance metric by executing a loss function algorithm, and wherein the defined bounds of the optimization threshold is a range less than or equal to a defined loss value.

A computer-implemented method for tuning a configuration setting of a radio frequency apparatus, the computer-implemented method comprising:

• applying a machine learning model to generate a test configuration setting for the radio frequency apparatus;
• generating performance evaluation data by operating the radio frequency apparatus with the test configuration setting;
• comparing the performance evaluation data to a target performance dataset to determine whether the test configuration setting is an optimal configuration setting for a defined objective;
• determining that the test configuration setting is sub-optimal based on a performance metric that characterizes performance data being less than the optimization threshold;
• and
• generating an updated machine learning model by adjusting one or more hyperparameters based on the performance evaluation data and the test configuration setting.

A computer program product for tuning configuration settings of a radio frequency apparatus, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to:

• control an operation of the radio frequency apparatus using an initial configuration setting;
• update a machine learning model based on performance evaluation data characterizing the operation of the radio frequency apparatus, wherein the machine learning model is updated by fitting the machine learning model to historic data that includes the performance evaluation data;
• and
• determine a test configuration setting for the radio frequency apparatus based on a prediction generated by the machine learning model regarding a second operation of the radio frequency apparatus using the test configuration setting.