Demodulation of Multi-Level Data using Convolutional Neural Network in Holographic Data Storage

Abstract

We evaluated a deep learning-based data demodulation method for multi-level recording data in holographic data storage. This method demodulates reproduced data as pattern recognition using a convolutional neural network. The network learns the rule of demodulation in consideration of optical noise that deteriorates the quality of reproduced data. Unlike with a conventional hard decision method, the learnt network demodulated the noise-added data accurately and decreased demodulation errors.