Channel modeling and estimation for intrapage equalization in pixel-matched volume holographic data storage

Abstract

We present two different channel models (the magnitude model and the intensity model) for a pixel-matched volume holographic data storage system that employs the 4-focal-length architecture. First, a framework to describe the channel models is developed. We evaluate the linearity of the channel models by comparing data values obtained from diffraction-limited interference with data values predicted by the channel models. The models are evaluated for linearity and equalization gain under different storage and read-back conditions, such as fill factors, apertures, and contrast ratios. Bit error rate results obtained by use of linear equalization methods in conjunction with the channel models developed are also presented. Our results suggest that the magnitude model leads to better performance when the fill factors are small, whereas the intensity model appears to be more appropriate for the high-fill-factor cases. The magnitude model, when suitable, appears to provide a storage density improvement of as great as 65%, whereas the intensity model seems capable of providing as much as 15% density gain through deconvolution. The optimum aperture for storage seems to be close to the Nyquist aperture.