Configuring Radio Resource Control Timers
卷宗概要
发明人
Yasser AlEryani; Satish Venkob; Mostafa Mouawad
IPC 分类
CPC 分类
A system can train and maintain a first deep reinforcement learning model, wherein the first deep reinforcement learning model was generated according to a first objective to improve timing of transitions from a radio resource control active state. The system can train and maintain a second deep reinforcement learning model, wherein the second deep reinforcement learning model was generated according to a second objective to improve timing of transitions from a radio resource control inactive state or a radio resource control idle state, and wherein the first deep reinforcement learning model and the second deep reinforcement learning model share an objective function. The system can determine respective timers for respective radio resource control states based on a first result of the training of the first deep reinforcement learning model and a second result of the training of the second deep reinforcement learning model.
原文（中文）
A system can train and maintain a first deep reinforcement learning model, wherein the first deep reinforcement learning model was generated according to a first objective to improve timing of transitions from a radio resource control active state. The system can train and maintain a second deep reinforcement learning model, wherein the second deep reinforcement learning model was generated according to a second objective to improve timing of transitions from a radio resource control inactive state or a radio resource control idle state, and wherein the first deep reinforcement learning model and the second deep reinforcement learning model share an objective function. The system can determine respective timers for respective radio resource control states based on a first result of the training of the first deep reinforcement learning model and a second result of the training of the second deep reinforcement learning model.