AI Channel Estimation Using Profile-Based MMSE Weights

The patent segments the channel estimation process into two distinct phases: (1) an offline training phase where a machine learning network is trained to map channel features to MMSE weights, and (2) an online estimation phase where the trained network quickly infers weights from received signals. This segmentation moves the computationally intensive work to the offline phase, making the online phase much simpler and more suitable for real-time deployment on devices with limited computational resources.

The patent performs preliminary action by pre-training the machine learning network offline to learn the optimal mapping between channel features and MMSE weights. During this offline training phase, the system processes大量 training data to build the neural network model. Once trained, the network contains embedded knowledge that enables fast, accurate channel estimation during online operation without requiring complex real-time computations, thus resolving the contradiction between accuracy and computational complexity.

The patent introduces a machine learning network as an intermediary between the received signals and the channel estimation process. Instead of directly computing complex second order statistics from received signals, the ML network acts as a mediator that automatically learns and extracts the necessary statistical features from the signals. This intermediary simplifies the operation by replacing difficult statistical computations with a trained model that performs the same function more easily, especially in challenging conditions like high noise and high mobility.

The patent replaces the mechanical/computational system of directly calculating second order statistics with a machine learning-based system. Instead of manually computing covariance matrices and other statistical measures, the ML network automatically infers the necessary information from channel features. This substitution transforms a complex mathematical computation problem into a pattern recognition problem that the trained network can solve more efficiently, improving ease of operation while maintaining estimation performance.