Machine learning assisted radio resource management (rrm) policies for high data rate low latency and other applications in o-ran networks

Abstract

A method for implementing enhanced radio resource management of open radio access network (O-RAN) based on machine-learning-based technique, includes: sending, from a distributed unit (DU) of the O-RAN to a traffic prediction analytics module, values of at least one network performance parameter comprising buffer occupancy (BO) for a plurality of data radio bearers (DRBs) at 5G Quality of Service Identifier (5QI) level; deploying, at the traffic prediction analytics module, a Long Short-Term Memory (LSTM) neural network comprising at least one LSTM unit for data traffic prediction of one of per-DRB data traffic or per-logical channel (LC) traffic for each one of a plurality of logical channels (LCs) based on the at least one network performance parameter; and deriving, by the traffic prediction analytics module based on at least the data traffic prediction, a set of parameters defining a policy for determining a scheduling priority of each one the LCs.