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Improvement of Iterative Decoding With LLR Modulator by Neural Network Using Magnetic Transition Information in SMR System

机译:基于SMR系统磁转换信息的神经网络与LLR调制器的迭代解码改进

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摘要

We have previously focused on a log-likelihood ratio (LLR) computed as the decoding reliability by $a$ posteriori probability (APP) decoder on the low-density parity-check (LDPC) coding and iterative decoding system for the shingled magnetic recording (SMR). Then, we clarified that the LLR modulator applying a neural network realizes an effective iterative decoding. In this article, we propose a new neural network LLR modulator considering magnetic transition information obtained from LLRs to reduce the influence of intersymbol interferences (ISIs).
机译:我们之前的重点集中在计算为Cododing可靠性的日志似然比(LLR)由<内联公式XMLNS:MML =“http://www.w3.org/1998/math/mathml”xmlns:xlink =“http ://www.w3.org/1999/xlink“> $ a $ postiori 概率(app)解码器上的低密度奇偶校验检查(LDPC)用于凸起磁记录(SMR)的编码和迭代解码系统。然后,我们阐明了应用神经网络的LLR调制器实现了有效的迭代解码。在本文中,我们提出了一种新的神经网络LLR调制器,考虑从LLR获得的磁转换信息,以减少Intersymbol干扰(ISIS)的影响。

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