2D Channel Identification, Equalization and Detection for Holographic Data Storage Systems

Holographic data storage is a promising data storage technology because of its potential for high data storage density (more than 1 Tbyte/in2), high data rate (more than 1 Gb/s) and short access time (less than lgs). In contrast to conventional data storage systems, holographic data storage systems use a parallel two-dimensional or page-like format in recording and retrieval. In this project, we have investigated different methods of 2D channel identification, equalization and detection for holographic data storage channel. To evaluate different 2D equalization and detection methods, we needed the channel model. Therefore, we first developed a physical channel model based on the physical impairments. We evaluated the performance of this physical channel model using 60 real recorded and retrieved pages, provided by Inphase Technologies. The advantage of the physical channel model is that it allows us to control the impairment amounts and study their impact. We also investigated linear and nonlinear channel identification methods and compared their performance. We investigated the minimum mean square error (MMSE) equalizer, the zero forcing equalizer (ZFE), the adaptive decision feedback equalizer (ADFE) and the adaptive Volterra equalizer (AVE). For detection methods, investigated fixed threshold, adaptive threshold, log likelihood ratio (LLR) and iterative detectors. To evaluate the performance of these equalization and detection techniques real pages as well as simulated pages (using physical channel model) have been used. The results show that MMSE equalizer outperforms the other equalizer for low signal to noise ratio (SNR) real data as well simulated data. Iterative detector works well for higher SNR simulated page but adaptive threshold detectors performs better for real data. Chapter