A system for antenna beam selection in a radio access network, the system comprising: a measurement module configured to obtain channel quality measurements of a plurality of beams usable to serve a user equipment in the radio access network; a training module coupled to the measurement module, wherein the training module is configured to generate a machine learning model based on the channel quality measurements; and a prediction module coupled to the training module, wherein the prediction module is configured to: receive the machine learning model from the training module, and select, based on the machine learning model, one of the plurality of beams used to serve the user equipment; wherein the measurement module is further configured to obtain and provide to the training module, during a training phase, all channel quality measurements of the plurality of beams, and wherein the training module is configured to update the machine learning model based on all channel quality measurements of the plurality of beams received from the measurement module; and wherein the prediction module is further configured to: continuously determine whether a new machine learning model is received from the training module, and when the channel quality measurements are obtained by the measurement module: send a request to the measurement module to provide, to the prediction module, channel quality of a subset of the plurality of beams, predict the selected beam based on the channel quality of the subset of the plurality of beams by feeding the channel quality measurements into the machine learning model, and output the selected beam to cause a baseband processing unit to switch its connection with the user equipment to the selected beam. a measurement module configured to obtain channel quality measurements of a plurality of beams usable to serve a user equipment in the radio access network; a training module coupled to the measurement module, wherein the training module is configured to generate a machine learning model based on the channel quality measurements; and a prediction module coupled to the training module, wherein the prediction module is configured to: receive the machine learning model from the training module, and select, based on the machine learning model, one of the plurality of beams used to serve the user equipment; receive the machine learning model from the training module, and select, based on the machine learning model, one of the plurality of beams used to serve the user equipment; wherein the measurement module is further configured to obtain and provide to the training module, during a training phase, all channel quality measurements of the plurality of beams, and wherein the training module is configured to update the machine learning model based on all channel quality measurements of the plurality of beams received from the measurement module; and wherein the prediction module is further configured to: continuously determine whether a new machine learning model is received from the training module, and when the channel quality measurements are obtained by the measurement module: send a request to the measurement module to provide, to the prediction module, channel quality of a subset of the plurality of beams, predict the selected beam based on the channel quality of the subset of the plurality of beams by feeding the channel quality measurements into the machine learning model, and output the selected beam to cause a baseband processing unit to switch its connection with the user equipment to the selected beam. continuously determine whether a new machine learning model is received from the training module, and when the channel quality measurements are obtained by the measurement module: send a request to the measurement module to provide, to the prediction module, channel quality of a subset of the plurality of beams, predict the selected beam based on the channel quality of the subset of the plurality of beams by feeding the channel quality measurements into the machine learning model, and output the selected beam to cause a baseband processing unit to switch its connection with the user equipment to the selected beam. send a request to the measurement module to provide, to the prediction module, channel quality of a subset of the plurality of beams, predict the selected beam based on the channel quality of the subset of the plurality of beams by feeding the channel quality measurements into the machine learning model, and output the selected beam to cause a baseband processing unit to switch its connection with the user equipment to the selected beam.