Deep Learning ADC Correction for Noise and Channel Mismatch
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Solution Overview
Problem
Current analog-to-digital converters (ADCs) face challenges in achieving high sampling rates and accurate signal conversion due to channel mismatch, nonlinearity, and noise distortion, particularly in wideband digital radar and high-speed communications, where existing methods struggle to minimize distortion effectively.
Innovation Solution
A deep learning-based method and device that incorporates a deep network with a microwave signal source, digital signal processor, and ADC to learn noise suppression and distortion correction, utilizing a convolutional or recurrent neural network for real-time signal processing to improve ADC performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If multi-channel architecture is used to increase sampling rate, then sampling rate is improved, but channel amplitude and time mismatch cause signal distortion
Solution Approach 1:
The patent applies preliminary action by training the deep neural network in advance using known input signals and their corresponding ADC output signals. During this training phase, the network learns to compensate for channel mismatches and nonlinearities before actual signal processing occurs. This pre-trained model then corrects distortion in real-time during operation, resolving the contradiction between high sampling rate and signal accuracy.
Solution Approach 2:
The deep neural network serves as an intermediary between the multi-channel ADC system and the final digital signal output. It processes the raw ADC outputs, compensating for channel amplitude and time mismatches, as well as nonlinear distortions from amplifiers and comparators. This intermediary processing enables the system to maintain high sampling rates while achieving accurate signal reconstruction.
2Measurement precision
If hardware parameters are adjusted to minimize distortion, then signal accuracy is improved, but system complexity and adjustment difficulty increase
Solution Approach 1:
The patent replaces complex hardware adjustment mechanisms with a software-based deep learning approach. Instead of physically adjusting amplifier gains, comparator thresholds, or timing circuits to minimize distortion, the system uses a trained neural network to digitally compensate for these effects. This substitution dramatically reduces system complexity while maintaining or improving signal accuracy.
Solution Approach 2:
The patent changes the approach from adjusting physical hardware parameters to modifying digital signal parameters through learned transformations. The deep neural network learns optimal parameter transformations during training and applies these digitally during operation, avoiding the complexity of physical parameter adjustments while achieving distortion minimization.
3Measurement precision
If deep learning technology is applied to ADC noise suppression, then signal accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by performing the computationally intensive deep learning training phase offline using known reference signals. Once trained, the neural network model is fixed and can be deployed for real-time noise suppression with minimal computational overhead during actual ADC operation. This separates the heavy computational burden from the real-time signal processing path.
Solution Approach 2:
The patent uses copying by creating a trained deep neural network model that captures the complex noise and distortion characteristics of the ADC system. This model copy can then be applied repeatedly to different input signals without requiring retraining, reducing computational complexity for each new signal while maintaining high signal accuracy through the learned noise suppression capabilities.
Data Source
AI summary
A device for noise suppression and distortion correction of analog-to-digital converters based on deep learning that realizes effect of correcting noise and distortion of analog to digital converters. The method is applied to electronic ADCs or photonic ADCs. It utilizes the learning ability of the deep network to perform system response learning on ADCs which need noise suppression and distortion correction, establishes a computational model in the deep network that can suppress the reconstruction of noises and distorted signals, performs noise suppression and distortion correction on the signals obtained by ADCs, and thereby improves performance of the learned ADCs. The device improves the performance of the microwave photon system with high sampling precision of microwave photon radar and optical communication system.