Neural Network RF Signal Denoising Without Noise Models
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Solution Overview
Problem
Existing methods for improving signal-to-noise ratio (SNR) in radio-frequency transmissions rely on prior information about the signal or noise, making blind SNR improvement challenging, especially in complex environments where signals and noise are mixed in complex ways.
Innovation Solution
A neural network, specifically a convolutional autoencoder, is trained to learn noise features from reference signals and apply these to denoise time-varying input signals without prior models, enabling end-to-end reconstruction and adaptation to uncharacterized noise sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard DSP methods (matched filtering, Fourier filtering) are used to improve SNR, then signal-to-noise ratio is improved, but prior information about the signal or noise is required
Solution Approach 1:
The neural network performs self-service by automatically learning noise features from reference signals without requiring external prior information about the signal or noise characteristics. The system trains the network to autonomously identify and remove noise patterns, making the denoising process self-sufficient and adaptable to unknown noise sources.
Solution Approach 2:
The invention changes the approach from using fixed prior knowledge parameters to dynamically learning noise features through neural network training. The network adapts its internal parameters (weights and biases) based on training data, allowing it to handle varying noise characteristics without requiring explicit prior information about the signal structure.
2Adaptability or versatility
If blind filtering methods are used without prior information, then adaptability to unknown noise is improved, but important signal features are likely to be filtered out
Solution Approach 1:
The neural network employs feedback mechanisms during training where the denoised output is compared with the original reference signal, and the error is used to adjust network parameters. This feedback loop enables the network to learn which features to preserve and which to remove, maintaining signal integrity while adapting to unknown noise patterns.
Solution Approach 2:
The system performs preliminary training action by training the neural network on reference signals that contain the same noise characteristics before actual denoising is applied. This preliminary training equips the network with knowledge of noise patterns, enabling it to effectively distinguish noise from signal features during subsequent operations without losing important information.
3Measurement precision
If prior information about signal features is used for denoising, then signal fidelity is improved, but the method cannot handle uncharacterized noise sources
Solution Approach 1:
The neural network achieves universality by being trained to handle multiple types of noise patterns through exposure to diverse reference signals during training. The learned features generalize across different noise sources, enabling the system to maintain high signal fidelity when encountering uncharacterized noise without requiring specific prior knowledge about the noise type.
Data Source
AI summary
Time-varying input signals are denoised by a neural network. The neural network learns features associated with noise added to reference signals. The neural network recognizes features of noisy time-varying input signals mixed with the noise that at least partially match at least some of the features associated with the noise. The neural network predicts denoised time-varying output signals that correspond to the time-varying input signals based on the recognized features of the noisy time-varying input signals that at least partially match at least some of the features associated with the noise.