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R&D: Deep Neural Network (DDN) Detection with ITI Subtraction for Non-Uniform Track-Width 2D Magnetic Recording

Article proposes DNN based detection deployed in conjunction with equalizer for TDMR system.

IEEE Transactions on Magnetics has published an article written by Chaiwat Buajong; College of Advanced Manufacturing Innovation, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand, Jaejin Lee; School of Electronic Engineering, Soongsil University, Seoul, South Korea, and Chanon Warisarn, College of Advanced Manufacturing Innovation, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand.

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 utilizes 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 involves 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 inter-track interference (ITI) subtraction technique, enhancing the information exchange. This technique can mitigate the ITI effect by subtracting the target signal with the imitated ITI signal. Additionally, 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. 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 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 provides performance gain as the number of iterations goes on.