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

Katano, Yutaro; Muroi, Tetsuhiko; Kinoshita, Nobuhiro; Ishii, Norihiko

doi:10.1109/dicta.2018.8615863

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…Then, Katano et al developed CNN demodulation by using a complex amplitude as established in [38]. In [39], the authors used a CNN to recognize the pattern of multi-level data in HDS systems.…”

Section: Reconstructedmentioning

confidence: 99%

A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems

Nguyen,

Lee

2023

Applied Sciences

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Central data systems require mass storage systems for big data from many fields and devices. Several technologies have been proposed to meet this demand. Holographic data storage (HDS) is at the forefront of data storage innovation and exploits the extraordinary characteristics of light to encode and retrieve two-dimensional (2D) data from holographic volume media. Nevertheless, a formidable challenge exists in the form of 2D interference that is a by-product of hologram dispersion during data retrieval and is a substantial barrier to the reliability and efficiency of HDS systems. To solve these problems, an equalizer and target are applied to HDS systems. However, in previous studies, the equalizer acted only as a linear convolution filter for the received signal. In this study, we propose a nonlinear equalizer using a convolutional neural network (CNN) for HDS systems. Using a CNN-based equalizer, the received signal can be nonlinearly converted into the desired signal with higher accuracy. In the experiments, our proposed model achieved a gain of approximately 2.5 dB in contrast to conventional models.

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Section: Reconstructedmentioning

confidence: 99%

A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems

Nguyen,

Lee

2023

Applied Sciences

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“…Figure 6 presents the demodulation network. The 3 × 3 symbols of the block as well as the neighboring symbols, containing information regarding the effects of IPI, are used to enable the CNN to learn the noise characteristics more accurately; thus, the input size is 5 × 5 11) . The CNN, which comprises convolutional and dense layers, previously learns the noise characteristics through a supervised learning using a dataset of the reproduced images of the data pages and the recorded original bit data as their label information.…”

Section: Proposed Decoding Systemmentioning

confidence: 99%

“…The retrieved data from the HDS are accurately demodulated by the CNN, based on the trained network. Although the CNN demodulation method is effective for multi-level amplitude modulation and binary modulation codes 11) , however, bit errors after the demodulation must be completely removed. Thus, the error correction code is important.…”

Section: Introductionmentioning

confidence: 99%

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

Katano¹,

Nobukawa²,

Muroi³

et al. 2021

MTA

Self Cite

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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.

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“…A variety of research frameworks have been proposed for HDS [9,10]. Various linked applications of HDS analyzed by researchers include digital holograms [11], deep learning [12,13] modulation code [14], digital watermarking [15], convolutional neural networks [16], and data compression methods [17]. Other research sets out to explore such aspects of HDS as fundamental issues [18], holographic memory [19], and bit error prediction [20], as well as focusing on holographic grating [21], fluid dynamics [22], and optical storage [23].…”

Section: Introductionmentioning

confidence: 99%

Bibliometric analysis of holographic data storage literature

Kumar

Parameswaran

2023

Rossijskij tehnologičeskij žurnal

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Objectives. Snapshots of data can be stored in a holographic medium at varying depths. Data can be written via a spiral data channel in spinning holographic media in the form of circular disks like CDs or DVDs. This data is then read by shining a reference beam through the refraction following writing. However, holographic storage is distinct from CD/DVD media in the sense that information is encoded in all three dimensions. Two-dimensional data is written using a single laser beam that spirals around the material. Prototype holographic storage solutions use minuscule cones formed by individual snapshots or pages to store one million pixels. As compared with magnetic disks and tapes, which have a finite lifespan of 50 years at most, the longevity and dependability of optical media storage is advantageous for long-term archiving. Holographic technology allows for the portability of data-intensive media such as broadcast or high-definition video. However, the shelf life of holographic media remains low due to its sensitivity to light. The primary goals of most storage devices are more storage space and faster data transport. Holographic storage devices have the potential to outperform traditional optical storage devices both in terms of capacity and performance. The present paper aims to evaluate the current international research trends in Holographic Data Storage (HDS) and produce a graphical mapping of co-authorship and countries.Methods. The major outputs of the dataset were authors, document type, publication, institution, nation, and citations. After exporting 1052 data sources, HistCite software was used to analyze the citations; visualization mapping was carried out using VOSviewer software and R programming language for the analysis of the authorcountry-title association on Holographic Storage Devices.Results. The most prominent authors, papers, journals, organizations, and nations in the field of HDS were identified in HistCite. Then, four clusters were investigated using VOSviewer based on author keywords, citation collaboration networks among different organizations, countries, and the HDS co-authorship network.Conclusions. During the study period from 2000–2020 (21 years), 4636 authors contributed to 1052 publications. The highest number of publications was in 2009, with a linear adjustment of R2 = 0.0136. The most prolific author, Lee J., published 3.14% of the articles on this subject. In terms of country distribution, Japan took first-place ranking, claiming 16.54% of the total number of articles. The “holographic” keyword was used in 62.55% of the articles.

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

Cited by 6 publications

References 19 publications

A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems

A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems

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

Bibliometric analysis of holographic data storage literature

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