Energy saving in cellular wireless networks via transfer deep reinforcement learning

Abstract

The present disclosure provides methods, apparatuses, systems, and computer-readable mediums for operating a target base station by an apparatus. A method includes collecting a plurality of trajectories corresponding to the target base station and a plurality of source base stations, clustering, using an unsupervised reinforcement learning model, the plurality of trajectories into a plurality of clusters including a target cluster, selecting, as a target trajectory, a selected trajectory from the target cluster that maximizes an energy-saving parameter of the target base station, and applying, to the target base station, an energy-saving control policy corresponding to the target trajectory. The target cluster corresponds to the target base station and at least one source base station from among the plurality of source base stations.