A Study on Performance Evaluation With Neural Network Detector in SMR System

Abstract

We previously applied a neural network detector (NND) to the low-density parity-check (LDPC) coding and iterative decoding system in a shingled magnetic recording (SMR) system. In this study, we evaluate the performance of iterative decoding using a new NND by computer simulation. The conventional NND gets the log-likelihood ratio (LLR) as the reliability of the sequence recorded from the two-dimensional finite impulse response (TD-FIR) filter output and the sum-product (SP) decoder output, but the new NND finds the LLR by adding the SP decoder input in order to consider the difference between the SP decoder input and the decoding result. Furthermore, we clarify that the new NND brings the almost same effect as the a posteriori probability (APP) decoder in the iterative decoding system.