Heat-assisted interlaced magnetic recording (HIMR) further increases recording density compared to heat-assisted magnetic recording due to the interlaced track layout. However, the smaller circular thermal profile of the top low-temperature-written tracks results in severe transition curvatures, which causes nonlinear distortions of the readback signal and degrades the bit error rate (BER) performance. Moreover, the increased recording density causes severe two-dimensional (2-D) intersymbol interference (ISI) along both down track and cross track directions. To mitigate the effect of nonlinear distortion and 2-D ISI in an HIMR system, we model a 2-D soft-transition, information-assisted, multitask neural network and modified Bahl–Cocke–Jelinek–Raviv (BCJR) detector (2DST-MTNN-MB) algorithm to detect three tracks simultaneously. The readback signal and 2-D soft-transition information are fed into the multitask neural network to obtain equalized signals and soft bit estimates of three tracks simultaneously. Then, the signal of the current track and soft estimates of the side tracks are embedded into the branch metrics of the modified BCJR detector for data detection, and the low-density parity check decoder is cascaded for error correction. The simulation results show that the 2DST-MTNN-MB algorithm provides 5.0 dB signal-to-noise ratio gains with reduced computation complexity compared with a single-track, single-task neural network and one-dimensional BCJR detector algorithm for the low-temperature-written track at channel bit density of 3.51 Tb/in 2 , thereby narrowing the gap of BER performance between high- and low-temperature-written tracks.

中文翻译：

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用于热辅助隔行磁记录的二维软过渡辅助多任务神经网络的多轨检测


由于交错磁道布局，与热辅助磁记录相比，热辅助交错磁记录（HIMR）进一步提高了记录密度。然而，顶部低温写入磁道的较小圆形热分布会导致严重的过渡曲率，从而导致读回信号的非线性失真并降低误码率（BER）性能。此外，增加的记录密度导致沿着下行磁道和跨磁道方向的严重的二维(2-D)符号间干扰(ISI)。为了减轻 HIMR 系统中非线性失真和 2-D ISI 的影响，我们对 2-D 软过渡、信息辅助、多任务神经网络和改进的 Bahl-Cocke-Jelinek-Raviv (BCJR) 检测器 (2DST- MTNN-MB）算法同时检测三个轨道。读回信号和二维软转换信息被馈送到多任务神经网络中，以同时获得三个轨道的均衡信号和软比特估计。然后，将当前磁道的信号和侧磁道的软估计嵌入到改进​​的BCJR检测器的分支度量中进行数据检测，并级联低密度奇偶校验解码器进行纠错。仿真结果表明，与单轨道、单任务神经网络和一维 BCJR 探测器算法相比，2DST-MTNN-MB 算法在低温环境下可提供 5.0 dB 的信噪比增益，同时降低了计算复杂度。写入磁道的通道位密度为3.51 Tb/in 2 ，从而缩小了高温和低温写入磁道之间的BER性能差距。