Multilayer perceptron-based detection for a coded two-dimensional magnetic recording

Recently, magnetic recording technology has been on the verge of reaching its recording density limit. Two-dimensional (2-D) magnetic recording (TDMR) is expected to be a candidate for next-generation magnetic recording because it provides powerful 2-D signal processing tools that are seamlessly compatible with the current technology. However, the degradation in signal quality due to 2-D interference when increasing the recording density far beyond the limit is inevitable even advanced signal processing tools cannot thoroughly handle such interference. In this work, we introduce two architectures of the multilayer perceptron (MLP)-based detection capable of producing soft information in the form of a log-likelihood ratio (LLR) for a coded TDMR system. This also enables turbo decoding capability to take place in the system, which further enhances the system performance. Such an architecture has two structures explicitly designed to execute before and during turbo decoding with a low-density parity-check code. In the first architecture, we employ triple MLP-based detection to process three readback sequences individually. For another architecture, it is designed to collec-tively process three readback sequences using just single MLP-based detection. The results show that both ar-chitectures of proposed detections outperform the systems using the conventional detection based on the Viterbi algorithm and single MLP-based detection achieves slightly better performance than triple MLP-based detection despite having much less complexity.