Learning to Reconfigure: Using Device Status to Select the Right Constrained Coding Scheme

2025-12-24

AI Summary

In two-dimensional magnetic recording (TDMR) systems, device aging induces time-varying channel characteristics, rendering conventional fixed-timing constraint-code switching strategies inadequate. Method: This paper proposes a dynamic constraint-code reconfiguration mechanism driven by real-time device state monitoring. It innovatively models the relationship between track density (TD) and bit error rate (BER) as a learnable variable and integrates a two-phase learning framework—combining offline training with online adaptation—alongside polynomial fitting, linear programming optimization, and LOCO coding to enable adaptive code-parameter reconfiguration. Contribution/Results: Theoretical analysis establishes global optimality of the proposed scheme. Experiments demonstrate that, compared to fixed-timing switching, the method increases storage capacity by 12.7% while maintaining reliability, reduces decoding complexity by 34.5%, and significantly enhances system robustness and adaptability to device lifetime degradation.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Search and Optimization: Mixed Discrete/Continuous Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Selecting optimal constrained coding schemes based on device status.
• Using offline and online learning to reconfigure coding for TDMR systems.
• Maximizing storage capacity and minimizing decoding complexity via optimization.

Innovation

• Learning-based reconfiguration using device status
• Polynomial fitting for bit error rate optimization
• Linear programming for optimal coding scheme switching