In this paper, we use the forward recursion BCJR algorithm to estimate the symmetric information rate for Two Dimensional Magnetic Recording (TDMR) channels. In particular, we consider a TDMR read/write channel whose all components, including recording medium, write and readback processes are modeled in software. Since the primary source of noise in TDMR arises from irregularities in the recording medium and leads to highly colored and data-dependent jitter, the pattern dependent noise predictive (PDNP) algorithm is implemented to improve the accuracy and performance of SIR estimation. Furthermore, we study the performance gain of using the PDNP algorithm in SIR estimation through simulations over the Voronoi based media model for different TDMR channel configurations.

With magnetic storage devices already achieving storage densities of up to 400 Gigabits per square inch (Gb/in2), the state of the art is rapidly approaching theoretical limits (dictated by thermal stability concerns). Hence, there is an eort in the industry to develop alternative magnetic storage technologies. Two-dimensional magnetic recording (TDMR) is one such candidate technology. In contrast to other technologies(e.g. heat-assisted magnetic recording [1], bit-patterned media [2]) which rely on signicant changes being made to the recording medium, TDMR relies on the use of traditional recording media, while relying on signal processing to make improvements in the recording density. Though advantageous due to the fact that no drastic re-engineering of media is required, there are signicant challenges that need to be addressed in order to make TDMR a viable candidate for next-generation recordingsystems.The main challenges involved in TDMR arise due to (i) the small bit-area, along with an aggressive write/read process, which leads to a large amount of noise, and (ii) the two-dimensional nature of the recording process { so far not encountered in today's systems. Thus, a gamut of 2D signal processing algorithms need be developed for the compensation of errors occurring due to the aggressive write/read processes. In this dissertation, we present some of the work done with regard to the signal processing tasks involved in TDMR. In particular, we describe our work on (i) channel modelling, (ii) detection strategies, and (iii) error-correction coding strategies targetted at TDMR.

Magnetic recording systems have reached a point where the grain size can no longer be reduced due to energy stability constraints. As a new magnetic recording paradigm, two-dimensional magnetic recording (TDMR) relies on sophisticated signal processing and coding algorithms, a much less expensive alternative to radically altering the media or the read/write head as required for the other technologies. Due to 1) the significant reduction of grains per bit, and 2) the aggressive shingled writing, TDMR faces several formidable challenges. Firstly, severe interference is introduced in both down-track and cross-track directions due to the read/write head dimensions. Secondly, reduction in the number of grains per bit results in variations of bit boundaries which consequently lead to data-dependent jitter noise. Moreover, the bit to grain ratio reduction will cause some bits not to be properly magnetized or to be overwritten which introduces write errors to the system. The nature of write and read processes in TDMR necessitates that the information storage be viewed as a two-dimensional (2D) system. The challenges in TDMR signal processing are 1) an accurate read channel model, 2) mitigating the effect of inter-track interference (ITI) and inter-symbol interference (ISI) by using an equalizer, 3) developing 2D modulation/error correcting codes matching the TDMR channel model, 4) design of truly 2D detectors, and 5) computing the lower bounds on capacity of TDMR channel. The work is concerned with several objectives in regard to the challenges in TDMR systems. 1. TDMR Channel Modeling: As one of the challenges of the project, the 2D Microcell model is introduced as a read channel model for TDMR. This model captures the data-dependent properties of the media noise and it is well suited in regard to detector design. In line with what has been already done in TDMR channel models, improvements can be made to tune the 2D Microcell model for different bit to grain densities. Furthermore, the 2D Microcell model can be modified to take into account dependency between adjacent microtrack borders positions. This assumption will lead to more accurate model in term of closeness to the Voronoi model. 2. Detector Design: The need for 2D detection is not unique to TDMR systems. However, it is still largely an open problem to develop detectors that are close to optimal maximum likelihood (ML) detection for the 2D case. As one of the important blocks of the TDMR system, the generalized belief propagation (GBP) detector is developed and introduced as a near ML detector. Furthermore, this detector is tuned to improve the performance for the TDMR channel model. 3. Channel Capacity Estimation: Two dimensional magnetic recording (TDMR) is a new paradigm in data storage which envisions densities up to 10 Tb/in² as a result of drastically reducing bit to grain ratio. In order to reach this goal aggressive write (shingled writing) and read process are used in TDMR. Kavcic et al. proposed a simple magnetic grain model called the granular tiling model which captures the essence of read/write process in TDMR. Capacity bounds for this model indicate that 0.6 user bit per grain densities are possible, however, previous attempt to reach capacities are not close to the channel capacity. We provide a truly two-dimensional detection scheme for the granular tiling model based on generalized belief propagation (GBP). Factor graph interpretation of the detection problem is provided and formulated in this section. Then, GBP is employed to compute marginal a posteriori probabilities for the constructed factor graph. Simulation results show huge improvements in detection. A lower bound on the mutual information rate (MIR) is also derived for this model based on GBP detector. Moreover, for the Voronoi channel model, the MIR is estimated for the case of constrained and unconstrained input. 4. Modulation Codes: Constrained codes also known as modulation codes are a key component in the digital magnetic recording systems. The constrained code forbids particular input data patterns which lead to some of the dominant error events or higher media noise. The goal of the dissertation in regard to modulation codes is to construct a 2D modulation code for the TDMR channel which improves the overall performance of the TDMR system. Furthermore, we implement an algorithm to estimate the capacity of the 2D modulation codes based on generalized belief propagation (GBP) algorithm. The capacity is also calculated in presence of white and colored noise which is the case for TDMR channel. 5. Joint Detection and Decoding Schemes: In data recording systems, a concatenated approach toward the constrained code and error-correcting code (ECC) is typically used and the decoding is done independently. We show the improvement in combining the decoding of the constrained code and the ECC using GBP algorithm. We consider the performance of a combined modulation constraints and the ECC on a binary-input additive white Gaussian noise (AWGN) channel (BIAWGNC) and also over one-dimensional (1D) and 2D ISI channels. We will show that combining the detection, demodulation and decoding results in a superior performance compared to concatenated schemes.

Two dimensional magnetic recording (TDMR) achieves high areal densities by reducing the size of a bit comparable to the size of the magnetic grains resulting in two dimensional (2D) inter symbol interference (ISI) and very high media noise. Therefore, it is critical to handle the media noise along with the 2D ISI detection. In this paper, we tune the generalized belief propagation (GBP) algorithm to handle the media noise seen in TDMR. We also provide an intuition into the nature of hard decisions provided by the GBP algorithm. The performance of the GBP algorithm is evaluated over a Voronoi based TDMR channel model where the soft outputs from the GBP algorithm are used by a belief propagation (BP) algorithm to decode low-density parity check (LDPC) codes.