SIGNAL PROCESSING AND CODING FOR TWO-DIMENSIONAL MAGNETIC RECORDING

Two-dimensional magnetic recording (TDMR) is a new technology to increase the areal density of magnetic recording systems with conventional magnetic disks. Signal processing and coding for the TDMR system is a key to increase the data storage capacity and reliability of data storage systems. In this dissertation, we propose several signal processing and coding approaches and also evaluate their performances via simulation.Initially, several turbo equalization systems for two-dimensional intersymbol interference (2D-ISI) channels are proposed. The systems' receivers consist of joint 2D-ISI equalizers with an irregular repeat accumulate (IRA) LDPC code.Then, a turbo equalization system for a TDMR Voronoi grain model is proposed. The proposed system exchanges log-likelihood ratios (LLRs) between a 2D-ISI equalizer and an IRA decoder. In order to consider the grain overwrite effect, the system employs a non-linear function to map 2D-ISI output LLRs to IRA decoder input LLRs. To pass back LLRs from the IRA decoder to the 2D-ISI equalizer, we design a simple LLR estimator.In addition, an extrinsic information transfer (EXIT) chart based IRA code design approach for TDMR Voronoi model is proposed. The receiver uses a 2D-ISI BCJR equalizer and IRA decoder. For one outer equalizer-decoder iteration, we propose theory and simulation based methods for computing EXIT curves. The simulation method calculates experimental EXIT curves for the check node decoder (CND) and the combination of the variable node decoder (VND) with equalizer. The theoretical approach recursively calculates CND and VND Gaussian mixture model parameters in order to calculate EXIT curves. We then fit the VND and CND EXIT curves to find optimized variable node degree distributions. We also derive optimized IRA codes for iterative turbo-equalization.Moreover, signal processing and coding approaches for a TDMR grain flipping probability (GFP) model is proposed. Three types of 2D-ISI detector are proposed for our system. We use a coset coding approach in our IRA decoder for decoding the received data. The read head sensitivity function is estimated using a least squares (LS) approach based on known data for a given set of reader outputs. The best achieved areal density is 1.7 Terabits/in$^2$ at 18nm track pitch