High noise margin decoding of holographic data page based on compressed sensing

In holographic data storage systems, the quality of the reconstructed data pattern is decisive and directly affects the system performance. However, noise from the optical component, electronic component and recording material deteriorates reconstruction quality. A high noise margin decoding method developed from compressed sensing technology was proposed to reduce the impact of noise in the decoding process. Compared with the conventional threshold decoding method, the proposed method is more robust to noise and more suitable for multilevel modulation. The decoding performance with five-level amplitude modulation was evaluated by both simulation and experimentation. For the combination of Gaussian noise, Rician noise and Rayleigh noise, the proposed decoding method reduces the BER of the threshold method to one-sixth with an SNR of -1 in the simulation. In the experiment, it behaves up to 8.3 times better than conventional threshold decoding.