Antenna-Based Online Neural Training for mmWave Channel Estimation
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately estimating channel characteristics due to variations in real-world radio environments, particularly in mmWave bands, which affect channel estimation performance.
Innovation Solution
An electronic device equipped with multiple antennas and neural networks performs online training using real-time signal analysis to estimate wireless communication channel characteristics, utilizing conditions such as CRC results and SNR to identify reliable training data and update neural network parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline training using pre-acquired data or link-level simulator is used, then training process is simple and fast, but channel estimation accuracy deteriorates due to inability to reflect real radio environment variations
Solution Approach 1:
The electronic device performs self-training by utilizing its own received signals and extracted channel characteristics as training data. The device extracts features from signals received through its antennas, uses these features to train the neural network model locally, and thereby improves channel estimation accuracy without requiring external training data or complex training systems.
Solution Approach 2:
The system implements a feedback mechanism where channel estimation results are continuously fed back into the training process. The neural network uses received signals and extracted channel characteristics as feedback to iteratively improve its model, allowing the system to adapt to real radio environment variations and enhance estimation accuracy over time.
2Measurement precision
If online training using real field data is performed, then channel estimation accuracy is enhanced by reflecting real radio environment, but training time and computational resources increase
Solution Approach 1:
Instead of requiring complete training datasets, the system performs partial online training using only the necessary channel characteristics extracted from current received signals. The neural network updates its parameters incrementally based on partial data samples, achieving improved accuracy without the time cost of processing entire datasets.
Solution Approach 2:
The system performs preliminary extraction of channel characteristics from received signals before feeding them into the neural network for training. By pre-processing and extracting only the essential features (such as channel impulse response, power delay profile, Doppler spread), the system reduces the computational burden and training time while maintaining the ability to reflect real radio environment variations.
3Measurement precision
If neural network parameters are continuously updated with new data, then model accuracy improves, but system stability and convergence may be affected
Solution Approach 1:
The system dynamically adjusts the training process based on the quality and relevance of incoming data. The neural network selectively updates parameters only when new channel characteristics indicate meaningful changes in the radio environment, rather than continuously updating with every data point. This dynamic approach maintains model stability while capturing real environment variations.
Solution Approach 2:
The system changes the training parameters and update frequency based on current operating conditions. When the radio environment is stable, the system uses fewer update cycles; when variations are detected, the system increases training intensity. This adaptive parameter adjustment balances model accuracy improvement with stability preservation.
Data Source
AI summary
An electronic device includes antennas; memory storing instructions; and a processor, wherein the instructions, when executed by the processor, cause the electronic device to receive a first signal from an external electronic device through an antenna; identify whether first information associated with a first characteristic of a first wireless communication channel identified from the first signal satisfies a condition for identifying reliability of training data; based on identifying that the first information satisfies the condition, perform online training of a first artificial neural network, for estimating a second characteristic of a second wireless communication channel corresponding to a cell, using the first information; and obtain, based on second information output from the first artificial neural network, an estimate of a third characteristic of a third wireless communication channel, based on the online training, by inputting a second signal received through an antenna into the first artificial neural network.