講演情報
[15p-K508-16]Deep Learning-Based Decoding Method of SQAM Signals for Holographic Data Storage
〇(M2)Jialin Zhang1, Hironori Ito1, Satoshi Honma1 (1.Yamanashi Univ.)
キーワード：
Holographic memory、Optical interferometor、Deep learning
Recording scheme of spatial quadrature amplitude modulation (SQAM) signals is a promising technique for high-capacity holographic memory. To detect the phase of reproduced signal, an interferometer is required. We proposed a batch reading and decoding (BRD) method, which captures the interference pattern of multiple reproduced SQAM signals to detect their phase distribution. By utilizing the holographic memory's optical system as an interferometer, the method simplifies the setup. However, complex signal processing, such as spatial frequency analysis, was needed to decode the complex amplitude values.
In this report, we propose a deep learning-based approach to directly decode SQAM signals from the interference intensity distribution, improving efficiency and simplifying the system.
In this report, we propose a deep learning-based approach to directly decode SQAM signals from the interference intensity distribution, improving efficiency and simplifying the system.