AI Channel Estimation Model for 5G Signal Reception

The patent replaces traditional mechanical/mathematical channel estimation methods (MMSE, LS) with an AI-based neural network model. The neural network learns optimal channel estimation mappings during training and performs rapid inference during operation, substituting complex iterative mathematical computations with a pre-trained intelligent model that achieves comparable or superior performance with reduced real-time computational burden.

The patent performs channel estimation computations in advance during the offline training phase. The neural network model is trained using extensive simulation data to learn optimal estimation patterns beforehand. During actual communication operation, the pre-trained model performs rapid inference without requiring complex real-time computations, thus resolving the contradiction between performance and computational complexity.

The patent replaces the simple but inaccurate LS method with an AI-based neural network model. The neural network learns optimal channel estimation mappings during training that account for complex channel characteristics, providing significantly improved estimation accuracy while maintaining low real-time computational complexity during inference operations.

The patent changes the fundamental approach from direct mathematical computation (LS) to learned parameter mapping through neural networks. The model learns optimal transformation parameters during training that adapt to various channel conditions, providing improved performance while maintaining computational efficiency during operation.

The patent performs the complex model training and optimization work in advance during the offline training phase. The neural network architecture, parameters, and structure are optimized beforehand using extensive simulation data. During actual communication operation, only lightweight inference is performed, which significantly reduces the computational burden and complexity on the communication devices while maintaining high estimation performance.

The patent replaces complex real-time channel estimation computations with a pre-trained AI model that performs rapid inference. The model captures complex channel characteristics during training and applies learned patterns during operation, substituting heavy computational tasks with efficient intelligent inference that achieves superior performance with reduced operational complexity.

The patent replaces traditional methods that require dense reference signals with an AI-based model that can achieve high estimation accuracy with fewer reference signals. The neural network learns to infer channel characteristics from limited observations during training, enabling accurate channel estimation with reduced reference signal overhead during actual operation.

The patent uses virtual reference signals generated through AI model inference to supplement physical reference signals. The neural network learns optimal channel characteristics during training and creates virtual reference signal patterns that can be used for channel estimation, effectively copying the information content of dense reference signals while using fewer actual transmitted reference signals.