Target design and low complexity signal detection for two-dimensional magnetic recording

Matcha, Chaitanya Kumar; Srinivasa, Shayan Garani

doi:10.1109/apsipa.2014.7041724

Cited by 6 publications

(7 citation statements)

References 14 publications

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“…The algorithm gives a 1 dB gain in performance over a single iteration of the JTED algorithm with the same computational complexity by considering a larger local span of symbols. The algorithm is within 1.5 dB of the full JTED performance [12].…”

Section: Our Contributions and Paper Organizationmentioning

confidence: 80%

“…Chen and Srinivasa [11] have proposed a 2-D joint equalization and detection (JTED) algorithm that combines a 2-D equalizer with multirow-multicolumn softin-soft-out detectors to iteratively achieve MAP performance. Matcha and Srinivasa [12] have recently proposed a low-complexity 2-D soft output Viterbi algorithm (SOVA), extending the ideas from the 1-D Viterbi algorithm [13], [14]. The algorithm is an approximation of the ML criterion, and it makes decisions by maximizing the likelihood probability of a local span of received samples.…”

Section: Our Contributions and Paper Organizationmentioning

confidence: 99%

“…1-D PR targets with integer coefficients were also studied [4]. Matcha and Srinivasa [12] have recently proposed techniques to design separable and nonseparable 2-D PR targets and PR equalizers under monic and unit energy constraints using the MMSE criterion over simple 2-D ISI channel with additive white Gaussian noise (AWGN). In this paper, our focus is to extend these techniques to TDMR channels using a Voronoi-based granular media model.…”

Section: Our Contributions and Paper Organizationmentioning

confidence: 99%

“…The PR target and the PR equalizer can be obtained by constrained minimization of the MSE as proposed in [12]. In the case of 2-D, an additional constraint of separability of the target response can be enforced that allows us to deploy low complexity detectors specifically optimized for separable 2-D ISI as in [9].…”

Section: B 2-d Pr Target Designmentioning

confidence: 99%

“…1) Nonseparable PR Targets: We derive the nonseparable PR targets under monic and unit energy constraints as in [12]. a) Unit energy constraint: Under the unit energy (g T g = 1) constraint, the MSE in (29) is minimized for…”

Section: B 2-d Pr Target Designmentioning

confidence: 99%

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Generalized Partial Response Equalization and Data-Dependent Noise Predictive Signal Detection Over Media Models for TDMR

Matcha

Srinivasa

2015

IEEE Trans. Magn.

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Two-dimensional magnetic recording (2-D TDMR) is an emerging technology that aims to achieve areal densities as high as 10 Tb/in 2 using sophisticated 2-D signal-processing algorithms. High areal densities are achieved by reducing the size of a bit to the order of the size of magnetic grains, resulting in severe 2-D intersymbol interference (ISI). Jitter noise due to irregular grain positions on the magnetic medium is more pronounced at these areal densities. Therefore, a viable read-channel architecture for TDMR requires 2-D signal-detection algorithms that can mitigate 2-D ISI and combat noise comprising jitter and electronic components. Partial response maximum likelihood (PRML) detection scheme allows controlled ISI as seen by the detector. With the controlled and reduced span of 2-D ISI, the PRML scheme overcomes practical difficulties such as Nyquist rate signaling required for full response 2-D equalization. As in the case of 1-D magnetic recording, jitter noise can be handled using a data-dependent noise-prediction (DDNP) filter bank within a 2-D signal-detection engine. The contributions of this paper are threefold: 1) we empirically study the jitter noise characteristics in TDMR as a function of grain density using a Voronoi-based granular media model; 2) we develop a 2-D DDNP algorithm to handle the media noise seen in TDMR; and 3) we also develop techniques to design 2-D separable and nonseparable targets for generalized partial response equalization for TDMR. This can be used along with a 2-D signal-detection algorithm. The DDNP algorithm is observed to give a 2.5 dB gain in SNR over uncoded data compared with the noise predictive maximum likelihood detection for the same choice of channel model parameters to achieve a channel bit density of 1.3 Tb/in 2 with media grain center-to-center distance of 10 nm. The DDNP algorithm is observed to give ∼10% gain in areal density near 5 grains/bit. The proposed signal-processing framework can broadly scale to various TDMR realizations and areal density points.Index Terms-2-D data-dependent noise prediction (DDNP), 2-D magnetic recording (TDMR), 2-D partial response maximum likelihood (PRML), 2-D soft output Viterbi algorithm (SOVA), media model.

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Section: Our Contributions and Paper Organizationmentioning

confidence: 80%