Deep Neural Network <i>a Posteriori</i> Probability Detector for Two-Dimensional Magnetic Recording
Jinlu Shen, Ahmed Aboutaleb, Krishnamoorthy Sivakumar, Benjamin J. Belzer, Kheong Sann Chan, Ashish James
Abstract
In two-dimensional magnetic recording (TDMR) channels, intersymbol interference (within and between tracks) and patterndependent media noise are impediments to reaching higher areal density. We propose a novel deep neural network (DNN)-based aposteriori probability (APP) detection system with parallel multi-track detection for TDMR channels. The proposed DNN-based APP detector replaces the trellis-based Bahl-Cocke-Jelinek-Raviv (BCJR) or Viterbi algorithm and pattern-dependent noise prediction (PDNP) in a typical TDMR scenario, in which it directly outputs log-likelihood ratios of the coded bits and iteratively exchanges them with a subsequent channel decoder to minimize bit error rate (BER). We investigate three DNN architectures-fully connected DNN, convolutional neural network (CNN), and long short-term memory (LSTM) network. The DNN's complexity is limited by employing linear partial response (PR) equalizer pre-processing. The best performing DNN architecture, CNN, is selected for iterative decoding with a channel decoder. Simulation results on a grain-flipping-probability (GFP) media model show that all three DNN architectures yield significant BER reductions over a recently proposed 2D-PDNP system and a previously proposed local area influence probabilistic (LAIP)-BCJR system. On a GFP model with 18 nm track pitch and 11.4 Teragrains/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the CNN detection system achieves an information areal density of 3.08 Terabits/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , i.e., a 21.72% density gain over a standard BCJR-based 1D-PDNP; the CNN-based system also has 3× the throughput of 1D-PDNP, yet requires only 1/10th the computer run time.