In the context of the information age, massive data growth has posed severe challenges to traditional storage technologies such as magnetic storage and hard disks, which are limited by low storage density, high energy consumption, and short service life, failing to meet the long-term and low-cost storage demands of large-scale datasets. Holographic data storage, as a promising optical storage technology with advantages of high density, long lifespan, and high speed, has become a key solution to address this bottleneck. Deep learning can be used to combine the holographic storage system to demodulate the complex amplitude information. This paper focuses on the correlation mechanism between the diffraction distance, diffraction intensity image, and deep learning decoding in the holographic storage system, and explores the differences in the representation of complex amplitude information by intensity images under different diffraction distances and the influence patterns on the deep learning model.

To achieve the research objectives, firstly, a theoretical analysis was conducted on the relationship between the oversampling rate and the diffraction distance. The non-interference complex amplitude decoding holographic storage system was emphasized, and the optimal diffraction distance of 20 for the oversampling rate was explored under this system. A decoding framework based on deep learning was constructed, leveraging the powerful image feature extraction and mapping capabilities of the U-Net neural network. Intensity diffraction images corresponding to different diffraction distances were collected, which contained the implicit phase and amplitude information of the complex amplitude data pages. These intensity diffraction images were used to train the U-Net model to explore the intrinsic correlation between the diffraction distance and the feature description of the complex amplitude. Additionally, by adjusting the oversampling rate parameters, the relationship between the oversampling rate and the diffraction distance was analyzed, and the symbol error rate (SER) decoding performance indicators were statistically compared under different experimental conditions to verify the relationship between the oversampling rate and the diffraction distance.

Firstly, it clarifies the correlation rule between oversampling rate and diffraction distance: the higher the oversampling rate, the longer the diffraction distance required to obtain an intensity diffraction pattern with a specific contrast. This is because a higher oversampling rate leads to a more compact distribution of spectral information, thus requiring a longer diffraction distance to separate the complex amplitude features hidden in the intensity image. Secondly, under specific conditions (oversampling rate of 20 and diffraction distance of 1.4 millimeters), the complex amplitude features carried by the intensity diffraction pattern match highly with the feature extraction mechanism of the U-Net neural network. This matching effect improves the efficiency of feature mapping of phase and amplitude information during model training, and makes the SER approach 0 in the early stages of iteration, which is superior to the decoding performance under other diffraction distance conditions. Finally, the oversampling rates of 10 and 30 correspond to the optimal diffraction distances of 0.4 mm and 3.1 mm respectively, further verifying the correlation rule between oversampling rate and diffraction distance elucidated in this paper.

This paper focuses on the core correlations between the diffraction distance, intensity diffraction pattern, and the specific Unet deep neural network decoding in the holographic storage system. It particularly explores the intrinsic relationship between the oversampling rate and the diffraction distance, and conducts quantitative analysis on the optimal diffraction distance under specific parameter combinations. The correlation rule between the oversampling rate and the diffraction distance is clarified: the higher the oversampling rate, the greater the diffraction distance required to obtain the specific contrast intensity diffraction pattern. This rule and the quantitative results of the optimal diffraction distance provide specific references for the selection of diffraction distance and parameter optimization in similar holographic storage systems, and lay the foundation for subsequent research on different encoding schemes and storage media.