CNN-based demodulation for a complex amplitude modulation code in holographic data storage

We developed a modulation code using a complex amplitude and established a method to demodulate the code based on a convolutional neural network (CNN) for holographic data storage. The developed 20:9 modulation code consists of nine symbols, each of which contains 4 bits of data representing the symbol position on which the complex amplitude is superimposed and 16 bits of data representing the actual complex amplitude value. By solving an optimization problem, the complex amplitude signal combines four amplitude values and a different phase value for each amplitude; thus, the data are robust against amplitude and phase noise, and the amplitude and phase values are distributed over a uniform distance in the constellation diagram. Modulation tables were also optimized using a genetic algorithm. Because the occurrence of bit errors due to amplitude and phase noise must be considered when reproducing data, two CNNs separately demodulate the symbol position signal and the complex amplitude signal superimposed thereon. By inputting reproduced data and label information indicating the demodulation target, we created a compact CNN. We confirmed that the CNN demodulation can accurately demodulate both signals; moreover, the total bit errors were reduced to less than half of those for the conventional hard decision demodulation method.

Automated summary

A modulation code using complex amplitude and a demodulation method based on CNN were developed for holographic data storage. The optimized complex amplitude signal is robust against noise, with amplitude and phase values distributed uniformly. By optimizing modulation tables and using CNNs, the total bit errors were significantly reduced compared to traditional methods.