Machine-learning frequency layer management and handover parameter tuning

Abstract

Machine-learning based techniques are described herein for determining and modifying handover parameters within multilayer wireless networks. Various communication session data, such as key performance indicators, may be analyzed and compared at multiple frequency layers to determine sets of custom parameters associated with one or more wireless networks. The sets of custom parameters and network performance data may be used to train one or more machine-learned models to improve and/or optimize the handover parameters used by the network nodes. In some examples, different trained models may be associated with different network performance metrics, such as throughput optimization, network speed, and/or dropped call minimization, etc. A trained machine-learned model may be used to analyze the session data from a set of network nodes, and to determine or tune the handover parameters used by the network nodes.