Noise Learning Denoising Autoencoder for Wireless Signal Reconstruction
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Solution Overview
Problem
Conventional denoising autoencoders face challenges such as high dimensionality of the latent space and the need for large training datasets, making them inefficient for wireless communication applications, particularly in regenerating original data from noisy inputs.
Innovation Solution
The noise learning-based denoising autoencoder (nlDAE) method learns noise through a neural network and subtracts it from noisy inputs to regenerate original data, optimizing parameters to minimize loss, which differs from conventional DAEs that directly learn original data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional DAE learns original data directly from noisy input, then reconstruction accuracy can be achieved, but the latent space dimension becomes too high and large training datasets are required
Solution Approach 1:
The patent extracts and learns only the noise component from the noisy input data, rather than attempting to learn the entire original data representation. By separating the noise learning task from the data reconstruction task, the method reduces the complexity of the latent space while maintaining reconstruction accuracy through the simple subtraction operation: x = y - n(x, y)
2Measurement precision
If conventional DAE learns original data directly, then good reconstruction performance can be achieved, but large training datasets are required
Solution Approach 1:
The patent extracts the noise component as a separate learnable entity from the complex original data structure. Since noise typically has simpler statistical properties and lower dimensionality than the full data signal, learning only noise requires significantly fewer training samples while still enabling accurate reconstruction through subtraction
Solution Approach 2:
The patent changes the learning target from the high-dimensional original data parameters to the lower-dimensional noise parameters. This parameter transformation reduces the complexity of the learning task and decreases the sample complexity required for effective training
3Adaptability or versatility
If conventional DAE uses high dimensional latent space, then stochastic features of original data can be captured, but computational efficiency and energy consumption deteriorate
Solution Approach 1:
The patent extracts only the noise component rather than modeling the entire data distribution, thereby capturing the essential stochastic degradation without requiring high-dimensional latent representations. This extraction approach maintains adaptability to stochastic noise while dramatically improving computational efficiency
Solution Approach 2:
Instead of directly modeling the complex original data distribution to capture its stochastic features, the patent inverts the approach by modeling the simpler noise distribution and subtracting it from the noisy input. This indirect approach achieves similar adaptability with much lower computational cost
Data Source
AI summary
Methods and apparatuses for noise learning-based denoising of noisy input data Y that is equal to the original data X plus the noise N (i.e., Y=X+N). In contrast with a conventional denoising autoencoder (DAE) method that attempts to learn the original data X directly from noisy input data Y, the noise learning-based denoising learns the noise N in the noisy input data Y and then regenerates the original data X by subtracting the learned noise N from the noisy input data Y. Learning the noise N may include inputting the noisy input data Y into an encoder of a neural network, and the learned noise N may be output from a decoder of the neural network. Training the neural network may include inputting noisy training data into an encoder of the neural network and outputting training noise from a decoder of the neural network.