Learning to Reconfigure: Using Device Status to Select the Right Constrained Coding Scheme
Doğukan Özbayrak, Ahmed Hareedy
TL;DR
This work addresses aging in two-dimensional magnetic recording (TDMR) by reconfiguring between constrained LOCO codes to maintain reliability while maximizing storage capacity. It introduces offline and online learning frameworks that fit bit-error rate (BER) as a function of TD density $D_{TD}$ with seventh-degree polynomials and solve convex optimization problems (reducing to linear programs) to determine optimal switching points among OP/SP/OT/ST LOCO codes. Offline learning yields globally optimal reconfiguration strategies under various objectives (capacity, capacity with complexity penalties, and capacity under adder-size constraints), while online learning adapts to data collected during device operation with multiple training-interval setups. The results demonstrate notable gains in capacity and reductions in encoding-decoding complexity compared to naive schemes and prior work, with online methods offering practical, data-efficient reconfiguration. The approach is broadly applicable to other constrained coding scenarios and can inform future integration with LDPC-based schemes for enhanced reliability and lifetime extension in storage systems.
Abstract
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.