[Paper] Efficient Decoding Method for Holographic Data Storage Combining Convolutional Neural Network and Spatially Coupled Low-Density Parity-Check Code

Abstract

In this study, we propose an effective data-decoding method for holographic data storage (HDS) by combining convolutional neural network (CNN) and spatially coupled low-density parity-check (SC-LDPC) code.The trained CNN provides output class probabilities and accurately demodulates the reproduced data from HDS.We focus on these probabilities, wherein only the untrainable noise components such as white Gaussian noise remain. These are used for calculating the log likelihood ratio in the sum-product decoding for the SC-LDPC code. We demonstrate an improvement of approximately 10 dB in the required signal-to-noise ratio for an errorfree decoding in numerical simulations.

Diffractive Deep-Neural-Network-Based Classifier for Holographic Memory

“…TIEs are a one-shot phase recovery method; however, a minimum of two diffraction patterns must be captured time-sequentially or using multiple image sensors. Recently, digital deep neural networks (DNNs) have been used as classifiers and equalizers and for the defocus correction and and super-resolution of readout data pages [10,11,[23][24][25][26][27][28][29][30][31][32][33]. For example, Ref.…”