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The grain flipping probability (GFP) model is used to reproduce the output of micromagnetic ( $\mu $ -mag) simulations several orders of magnitude faster. In this paper, we compare signals from the $\mu $ -magsimulations and the GFP model with readback from a Seagate hard disk drive (HDD). We validate our models by benchmarking the model output against the HDD readback in terms of magnetic write width, and signal-to-noise ratio and bit error rate as a function of the overshoot triplet parameters.

Perpendicular magnetic recording (PMR) in the hard disk drive is approaching its physical limits. In an earlier work, the dedicated servo (DS) recording system has been proposed to provide continuous position error signal for servo, enable higher servo sampling rate, and improve the overall servo performance. A further benefit is that the DS layer results in surface area savings at the data layer. However, it was also reported that the embedded servo layer introduces baseline variation and non-linear transition shift (NLTS) to the readback signal of the data layer. In this paper, we propose novel signal processing techniques to improve the bit error rate (BER) in DS recording. The synchronous averaging technique is proposed to improve the BER in the presence of baseline variation distortions. Further, the servo and data-dependent noise prediction method is proposed to mitigate the effect of the NLTS. Through the use of these techniques the linear density loss from the conventional PMR media is reduced.