Noise Learning-Based Denoising Autoencoder

Woong-Hee Lee,Mustafa Ozger,Ki Won Sung,Ursula Challita

doi:10.1109/lcomm.2021.3091800

Woong-Hee Lee, Mustafa Ozger + Show 2 more

Open Access

https://doi.org/10.1109/lcomm.2021.3091800

Copy DOI

Abstract

This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE learns the noise of the input data. Then, the denoising is performed by subtracting the regenerated noise from the noisy input. Hence, nlDAE is more effective than DAE when the noise is simpler to regenerate than the original data. To validate the performance of nlDAE, we provide three case studies: signal restoration, symbol demodulation, and precise localization. Numerical results suggest that nlDAE requires smaller latent space dimension and smaller training dataset compared to DAE.

Highlights

M ACHINE learning (ML) has recently received much attention as a key enabler for future wireless communications [1]–[3]
Denoising autoencoder (DAE) is a promising technique to improve the performance of Internet of Things (IoT) applications by denoising the observed data that consists of the original data and the noise [4]
3) Experimental Parameters: We evaluate the performance of the proposed noise learning based DAE (nlDAE) in terms of the mean squared error (MSE) of restoration

Summary

Introduction

M ACHINE learning (ML) has recently received much attention as a key enabler for future wireless communications [1]–[3]. While the major research effort has been put to deep neural networks, there are enormous number of Internet of Things (IoT) devices that are severely constrained on the computational power and memory size. DAE is a neural network model for the construction of the learned representations robust to an addition of noise to the input samples [5], [6]. The representative feature of DAE is that the dimension of the latent space is smaller than the size of the input vector. It means that the neural network model is capable of encoding and decoding through a smaller dimension where the data can be represented

Objectives

Methods

Conclusion