WTRU MIMO Precoder Codebook Design via Deep Reinforcement Learning

The patent implements dynamic precoder selection by training a neural network model that continuously adapts to time-varying channel conditions. The model processes current channel state information and generates precoder recommendations that evolve with changing channels, replacing static traditional feedback mechanisms with a dynamic adaptive system that maintains optimal performance across varying conditions.

The patent substitutes traditional mechanical feedback mechanisms with an intelligent neural network-based system. Instead of relying on conventional feedback loops and manual precoder selection, the system uses machine learning algorithms that automatically learn optimal precoder mappings from channel data, replacing the mechanical feedback system with an intelligent adaptive system that achieves superior performance.

The patent applies preliminary action by training the neural network model offline before deployment. The model is pre-trained using historical channel data and simulation results to learn optimal precoder selection strategies. This preliminary training phase allows the system to acquire sophisticated channel adaptation capabilities without adding real-time computational complexity during actual operation, as the heavy learning work is completed in advance.

The neural network model is designed to be self-sufficient once trained, automatically processing channel state information and generating precoder recommendations without requiring complex real-time training or extensive computational resources during operation. The model serves itself by making autonomous decisions based on learned patterns, reducing the need for complex control systems and manual intervention.