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Reduced Complexity Multi-Track Joint Detector for Sidetrack Data Estimation in High Areal Density BPMR

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For the high areal density bit patterned media recording (BPMR) systems, various multi-track equalization and detection techniques were proposed to mitigate the inter-track interference (ITI) so as to recover recorded… Click to show full abstract

For the high areal density bit patterned media recording (BPMR) systems, various multi-track equalization and detection techniques were proposed to mitigate the inter-track interference (ITI) so as to recover recorded data from the detecting tracks efficiently, but most works rarely aim to recover the recorded data on the sidetracks concurrently. As anticipation of that the readback signal has the intense contributions from the sidetracks due to high areal density, in this paper, we attempt to estimate the recorded data from the sidetracks as well as the detecting tracks by a multi-track joint detector that processes multi-track readback signals jointly in a single detector. To avoid the high complexity, which is the main shortfall in realizing the multi-track joint detector, we propose to employ a multi-track joint detector with a reduced complexity trellis for multi-head multi-track systems, in particular, four-head four-track (4H4T) BPMR system. To reduce the number of states in the trellis, the signals are equalized to 2-D generalized partial response targets whose cornered ITI coefficients are set to zero. The parallel branches are also incorporated in the trellis to estimate the data on the sidetracks. Moreover, we propose a two-head four-track (2H4T) system which generates the estimates of the recorded data on the four tracks by processing the signals from only two tracks. According to the simulation results, the proposed detector performs well on the data estimation of the detecting tracks as well as the sidetracks in both 4H4T and 2H4T systems.

Keywords: track; joint detector; multi track; track joint

Journal Title: IEEE Transactions on Magnetics
Year Published: 2018

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