Reference Signal Training for AI-Based Channel Estimation
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Solution Overview
Problem
Existing wireless communication technologies face challenges in improving channel estimation performance due to limitations in training methods and measurement performance of artificial neural networks, particularly in dynamic and complex radio channels, and there is a need for efficient interference and noise cancellation using AI and ML technologies.
Innovation Solution
A method and apparatus for channel information transfer using artificial neural networks trained based on reference signals, where terminals receive configuration information and training signals to enhance interference cancellation and noise reduction, utilizing techniques like self-supervised learning and auto-encoders to improve channel estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If artificial neural networks are applied to remove interference and noise on channel, then channel estimation performance is improved, but training method improvement and measurement performance enhancement are required which increases system complexity
Solution Approach 1:
The base station transmits reference signals in advance specifically for training the neural network model at the terminal before actual channel estimation is performed. This preliminary transmission of training signals enables the terminal to pre-train the interference cancellation model, resolving the complexity issue by separating training phase from estimation phase.
Solution Approach 2:
Reference signals serve as an intermediary element that enables neural network training without requiring direct access to actual channel realizations. These reference signals act as a mediator between the base station and terminal, providing the necessary training data to improve channel estimation performance while managing system complexity.
2Measurement precision
If neural networks are trained using actual channel data, then measurement performance is improved, but obtaining accurate training data in dynamic radio channels becomes difficult
Solution Approach 1:
Instead of using actual channel realizations for training, the system creates a simplified copy or representation of the channel through reference signals. These reference signals replicate the essential channel characteristics in a controlled manner, enabling neural network training without the difficulty of obtaining accurate real-world channel data.
Solution Approach 2:
The training process is segmented from the actual communication process. Reference signals are transmitted separately and specifically for training purposes, allowing the neural network to be trained on isolated, controlled data before being applied to actual channel estimation, thus overcoming the difficulty of obtaining training data in dynamic channels.
3Measurement precision
If reference signals are transmitted frequently for training, then neural network training accuracy is improved, but transmission resources are consumed
Solution Approach 1:
The system transmits reference signals with appropriate frequency and duration to achieve sufficient training accuracy without excessive resource consumption. By optimizing the amount of training data transmitted, the system achieves the minimum necessary reference signal transmissions to train the neural network effectively while avoiding wasteful over-transmission of resources.
Data Source
AI summary
Disclosed are techniques for channel information transfer in a communication system. A method of a terminal may comprise: receiving, from a base station, first signal configuration information including transmission resource information of a first signal indicated for training; receiving, from the base station, at least one first signal according to the transmission resource information; and training an interference cancellation artificial neural network based on the at least one first signal.