학술논문
Reduced Complexity Neural Network Equalizers for Two-dimensional Magnetic Recording
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Working Paper
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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.
Comment: This paper has been accepted for publication in IEEE Transactions on Magnetics. Part of this paper was presented in the 33rd magnetic recording conference (TMRC) 2022, on August 29, 2022
Comment: This paper has been accepted for publication in IEEE Transactions on Magnetics. Part of this paper was presented in the 33rd magnetic recording conference (TMRC) 2022, on August 29, 2022