Channel Estimation Neural Network for MIMO-OFDM Systems

The patent transforms the channel estimation problem from a linear domain to a neural network parameter space. By training neural network parameters offline to capture nonlinear channel characteristics, the online estimation achieves high accuracy without complex real-time nonlinear computations, thus resolving the contradiction between computation overhead and estimation accuracy.

The patent performs preliminary training of the neural network model offline using channel data to learn nonlinear features. This preliminary action stores the computational complexity in the training phase, enabling simple and accurate online estimation by directly applying the pre-trained model, thereby reducing real-time computation overhead while maintaining high accuracy.

The patent replaces the traditional linear mathematical estimation mechanism with a neural network-based estimation mechanism. The neural network automatically learns and adapts to nonlinear channel characteristics through training, substituting complex manual linear processing with an intelligent system that handles nonlinearities naturally, thus improving accuracy without proportionally increasing device complexity.

The patent changes the estimation approach from fixed linear parameters to adaptive neural network parameters. By training the network to learn optimal parameters for specific channel conditions, the system achieves high accuracy in nonlinear fading channels while keeping the inference complexity manageable through efficient network architectures.

The patent performs the computationally intensive neural network training phase offline in advance. The trained model parameters are then deployed for online channel estimation, where only lightweight forward propagation is required. This preliminary action shifts the computational burden from the resource-constrained online phase to the offline phase, resolving the contradiction between accuracy and computation overhead.

The patent optimizes the neural network parameters through offline training to achieve the best trade-off between accuracy and computational efficiency. By adjusting network architecture, activation functions, and training parameters, the system achieves high estimation accuracy while minimizing the computational overhead during online operation.