Reduced Complexity Neural Network Equalizers for Two-dimensional Magnetic Recording

Abstract

This paper investigates reduced complexity neural network (NN) based architectures for equalization over the two-dimension magnetic recording (TDMR) digital communication channel for data storage. We use realistic waveforms measured from a hard disk drive (HDD) with TDMR technology. We show that the multilayer perceptron (MLP) non-linear equalizer achieves a 10.91\% reduction in bit error rate (BER) over the linear equalizer with cross-entropy-based optimization. However, the MLP equalizer's complexity is 6.6 times the linear equalizer's complexity. Thus, we propose reduced complexity MLP (RC-MLP) equalizers. Each RC-MLP variant consists of finite-impulse response filters, a non-linear activation, and a hidden delay line. A proposed RC-MLP variant entails only 1.59 times the linear equalizer's complexity while achieving a 8.23\% reduction in BER over the linear equalizer.

Tasks

Reproductions