Reinforcement learning based inter-radio access technology load balancing under multi-carrier dynamic spectrum sharing

Abstract

A method may include receiving, at a reinforcement learning load balancer, a load metric related to available resources of a carrier. The method may also include calculating, based on the load metric, an update to carrier assignment parameters for a user. The method may further include calculating, based on the load metric, an update to uplink/downlink split proportions and resource pool split proportions. In addition, the method may include predicting an impact of the update to the carrier assignment parameters, the update to the uplink/downlink split proportions, and the update to the resource pool split proportions to the load metric. Further, the method may include communicating the update to carrier assignment parameters, the update to uplink/downlink split proportions, and the update to resource pool split proportions to one or more radio resource managers of corresponding one or more radio access technologies for application to a communication network.