Iterative decision feedback equalizer detector for holographic data storage systems

Abstract

In this study, we analyze and compare the performance of fixed threshold detection, adaptive threshold detection, log likelihood ratio (LLR) detection and iterative decision feedback equalizer (DFE) detection methods for holographic data storage (HDS) systems using real data pages as well as simulated data pages. Fixed threshold detector is popular because of its simplicity. Its performance is good when the amount of noise and inter-symbol interference (ISI) is low. Adaptive threshold is useful as a benchmark to compare the performance of other single-bit detectors, but is really not practical. LLR detector makes a decision based on bit probabilities and in that sense is an optimal detector when the input symbols are equally likely. LLR detector is usually used in combination with an iterative soft decoder. Iterative DFE detector takes into account the influence of the neighboring bits and the two dimensional (2D) ISI. Therefore an iterative DFE detector is a good detector candidate for HDS. The four detectors are tested with real data pages as well as simulated data pages. The results show that iterative DFE-based detector works well, even for data pages with low signal-to-noise ratio (SNR).