Deep Neural Network Detection With an ITI Subtraction for Non-Uniform Track-Width Two-Dimensional Magnetic Recording

Abstract

Continuously expanding the magnetic recording density results in unavoidable interferences including intersymbol interference (ISI) and intertrack interference (ITI) that both critically degrade the system performance. Even with advanced signal processing tools, two-dimensional magnetic recording (TDMR) still struggles to provide satisfactory performance. Thus, this article proposes the deep neural network (DNN)-based detection deployed in conjunction with an equalizer for the TDMR system. The coding scheme uses a low-density parity-check (LDPC) code, enabling the information exchange or the turbo decoding. The retrieval of data occurs within a group of three adjacent tracks. We also explore two different track configurations: uniform and non-uniform tracks that involve doubling the width of the middle track among the three adjacent tracks. The utilization of the highly reliable signal obtained from the double-width track enables the application of ITI subtraction technique, enhancing the information exchange. This technique can mitigate the ITI effect by subtracting the target signal with the imitated ITI signal. In addition, we investigate two different DNN architectures including the multilayer perceptron (MLP) and convolutional neural network (CNN), along with two scenarios for the detections in different passes of turbo decoding. The simulation results conducted on the Voronoi media model, with realistic grains and non-magnetic grain boundaries, show that the proposed detection systems with the non-uniform track configuration offer a performance gain up to 5.3 dB over the system with the uniform track configuration. Moreover, iteration for the turbo decoding passes incrementally improves the system performance in the proposed systems with the non-uniform track while the systems with the uniform track no longer provide performance gain as the number of iterations goes on.