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

Abstract

LDPC (SC-LDPC) code 15) is one of the strongest error correction codes that approaches the Shannon limit, based on the LDPC code 16) . We confirmed that the capability of error correction of the SC-LDPC code outperforms that of the LDPC code in the HDS 17) . This study presents an effective data-decoding method by combining the CNN demodulation and SC-LDPC code to enable a more powerful error correction by using the likelihood information obtained as the output from the CNN. We evaluated the characteristics of the demodulation and error correction method using the reproduced data with numerically added noise. 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 error-free decoding in numerical simulations.