In the age of data revolution, a modern storage~or transmission system typically requires different levels of protection. For example, the coding technique used to fortify data in a modern storage system when the device is fresh cannot be the same as that used when the device ages. Therefore, providing reconfigurable coding schemes and devising an effective way to perform this reconfiguration are key to extending the device lifetime. We focus on constrained coding schemes for the emerging two-dimensional magnetic recording (TDMR) technology. Recently, we have designed efficient lexicographically-ordered constrained (LOCO) coding schemes for various stages of the TDMR device lifetime, focusing on the elimination of isolation patterns, and demonstrated remarkable gains by using them. LOCO codes are naturally reconfigurable, and we exploit this feature in our work. Reconfiguration based on predetermined time stamps, which is what the industry adopts, neglects the actual device status. Instead, we propose offline and online learning methods to perform this task based on the device status. In offline learning, training data is assumed to be available throughout the time span of interest, while in online learning, we only use training data at specific time intervals to make consequential decisions. We fit the training data to polynomial equations that give the bit error rate in terms of TD density, then design an optimization problem in order to reach the optimal reconfiguration decisions to switch from a coding scheme to another. The objective is to maximize the storage capacity and/or minimize the decoding complexity. The problem reduces to a linear programming problem. We show that our solution is the global optimal based on problem characteristics, and we offer various experimental results that demonstrate the effectiveness of our approach in TDMR systems.

翻译：在数据革命时代，现代存储或传输系统通常需要不同级别的保护。例如，当设备处于全新状态时，用于加固现代存储系统中数据的编码技术，不能与设备老化时使用的技术相同。因此，提供可重构的编码方案并设计有效的方法来执行这种重构，是延长设备寿命的关键。我们专注于针对新兴的二维磁记录（TDMR）技术的约束编码方案。最近，我们为TDMR设备生命周期的各个阶段设计了高效的字典序约束（LOCO）编码方案，重点在于消除隔离模式，并通过使用这些方案展示了显著的性能增益。LOCO编码天然具有可重构性，我们在工作中利用了这一特性。基于预定时间戳的重构（行业目前采用的方法）忽略了设备的实际状态。相反，我们提出了基于设备状态的离线和在线学习方法来执行此任务。在离线学习中，我们假设在整个感兴趣的时间跨度内训练数据都可用；而在在线学习中，我们仅使用特定时间间隔的训练数据来做出关键决策。我们将训练数据拟合到多项式方程中，这些方程给出了误码率与TD密度的关系，然后设计一个优化问题，以得出从一个编码方案切换到另一个的最佳重构决策。目标是最大化存储容量和/或最小化解码复杂度。该问题可简化为一个线性规划问题。我们证明了基于问题特性，我们的解是全局最优的，并提供了各种实验结果，展示了我们的方法在TDMR系统中的有效性。