Neural Adaptive Line Enhancement for In-Band RF Noise Suppression
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Solution Overview
Problem
Conventional adaptive line enhancers (ALEs) are ineffective against in-band radio frequency interference, leading to degraded performance in audio quality and system instability due to nonlinear distortions introduced by analog front-end circuits.
Innovation Solution
Employing a neural-network-based adaptive line enhancer (NN-ALE) that de-correlates input signals using a delay and generates noise estimates to suppress in-band RF interference, overcoming nonlinear distortions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional adaptive filters are used to suppress noise, then noise cancellation is achieved, but computational cost increases significantly
Solution Approach 1:
The patent replaces conventional adaptive filters with a neural network-based system that uses deep learning models (such as U-Net, ResNet, or Transformer architectures) to perform noise cancellation. This substitution transitions from traditional signal processing mechanisms to AI-based computational models, achieving effective noise suppression while managing computational requirements through optimized network designs and training procedures
2Adaptability or versatility
If FIR-based adaptive line enhancers are used, then periodic and stochastic components are separated, but in-band RF interference degrades performance
Solution Approach 1:
The patent replaces FIR-based adaptive line enhancers with neural network-based systems that can learn and adapt to complex signal patterns. The neural networks are trained to distinguish between periodic components, stochastic components, and in-band RF interference, enabling effective signal separation while maintaining robustness against RF interference through learned representations and features
Solution Approach 2:
The patent employs dynamic parameter adjustment in the neural network system, where the model adapts its internal parameters during inference to optimize performance under varying interference conditions. This allows the system to adjust its noise cancellation characteristics in real-time based on the input signal and interference profile, maintaining effectiveness against in-band RF interference
3Reliability
If adaptive filters automatically adjust parameters, then noise is removed from input signal, but system stability decreases due to in-band RF interference
Solution Approach 1:
The patent substitutes traditional adaptive filters with a neural network-based system that processes signals through learned transformations. The neural network architecture incorporates stabilization mechanisms such as batch normalization, dropout, and carefully designed loss functions that promote stable convergence and prevent oscillations, thereby maintaining system stability while effectively removing noise
Solution Approach 2:
The patent implements feedback mechanisms in the neural network system through techniques such as backpropagation during training and inference-time adjustments based on signal characteristics. The system continuously monitors the input signal and adapts its processing parameters accordingly, providing feedback control that maintains stability while achieving effective noise removal under varying conditions
Data Source
AI summary
A method for reducing noise using a neural-network-based adaptive line enhancer includes receiving an input signal including a narrowband signal and noise. The narrow signal and the noise are de-correlated via an artificial neural network. The artificial neural network generates an estimate of the narrowband signal. The noise in the input signal is reduced based on the estimated narrowband signal.