Data page reconstruction method based on two-dimensional soft output Viterbi algorithm with self reference for holographic data storage

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.

Section: Reconstructedmentioning

confidence: 99%

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

Nguyen,

Lee

2023

“…Then, the read-back signal suffers from blurring effects (2D ISI), additive noise, and misalignment. Thus, the signal y[j,k] is presented as follows [23][24][25]:…”

Section: Proposed Modelmentioning

confidence: 99%

Simplified Two-Dimensional Generalized Partial Response Target of Holographic Data Storage Channel

Nguyen

Lee

2022

With a high capacity and fast data access rate, holographic data storage (HDS) is a potential candidate for future storage systems. However, for page-oriented data processing, two-dimensional (2D) interference appears intensely in the HDS systems. Therefore, the new 2D generalized partial response (GPR) target is introduced to estimate the 2D interference. In addition, we also propose a method to analyze the 2D GPR target into two serial one-dimensional (1D) GPR targets. It makes us design a simple detection scheme composed of two serial 1D detectors instead of a complicated 2D detector. In simulations, the results show that our proposed scheme can improve the BER performance compared to the conventional 1D GPR target model.

“…In addition, modulation and error correcting codes have been proposed to solve the 2D ISI [8][9][10][11]. Specifically, in signal processing, the equalizer combined with the partial response maximum likelihood (PRML) reduces the 2D ISI significantly [3,[12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Interference Estimator with Parallel Structure for Holographic Data Storage Channel

Nguyen

Lee

2022

Recently, holographic data storage (HDS) has attracted significant attention as the next-generation storage technology because of its large capacity and high-speed transfer capabilities. However, two-dimensional (2D) inter-symbol interference (ISI) is a critical problem encountered in HDS. Therefore, in this study, we propose a 2D interference estimator to prevent the 2D ISI. In addition, we propose a parallel structure to exploit horizontal interference (HI) and vertical interference (VI) effectively. After detecting the HI and VI, the signal is combined using an average function. The simulation results indicate that the proposed model can significantly improve the performance of HDS.