Determining reject wait times for radio resource control idle and inactive user equipment in advanced communication networks

Abstract

The technology described herein is directed towards near-instantaneous optimization of radio resource control (RRC) rejection wait times per user equipment, which can be per user equipment of a group of multiple user equipment. In inference mode, a trained wait time prediction model (e.g., an artificial intelligence/deep reinforcement learning (AI/DRL) model in a base station) adaptively determines the per-UE rejection wait times based on current (measured) network state data, including current centralized unit and distributed unit utilization levels. The technology described herein operates to decreases the overall RRC request rate, while maintaining very high utilization of gNodeB resources, which results in increased per-UE energy efficiency and decreased signaling traffic loads.