Iven Guzel

dblp:353/1132 · also Iven Güzel · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0002-0996-4549ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Coding theory · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Storage systems · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory
constrained coding
0.812024
Eliminating Media Noise While Preserving Storage Capacity: Reconfigurable Constrained Codes for Two-Dimensional Magnetic Recording · IEEE Trans. Inf. Theory 2024
Storage systems
magnetic recording
0.212024
Eliminating Media Noise While Preserving Storage Capacity: Reconfigurable Constrained Codes for Two-Dimensional Magnetic Recording · IEEE Trans. Inf. Theory 2024
Storage systems › magnetic recording
two-dimensional magnetic recording
0.212024
Eliminating Media Noise While Preserving Storage Capacity: Reconfigurable Constrained Codes for Two-Dimensional Magnetic Recording · IEEE Trans. Inf. Theory 2024

Methods — techniques the papers use, named apart from their topics

lexicographically-ordered codes · 1.5constrained coding · 1.5
YearPublicationVenuePosition
2024 Eliminating Media Noise While Preserving Storage Capacity: Reconfigurable Constrained Codes for Two-Dimensional Magnetic Recording
abstract
Magnetic recording devices are still competitive in the storage density race with solid-state devices thanks to new technologies such as two-dimensional magnetic recording (TDMR). TDMR offers remarkable storage density increase without the need for new magnetic materials; however, advanced data processing schemes are needed to guarantee reliability. Data patterns where a bit is surrounded by complementary bits at the four positions with Manhattan distance 1 on the TDMR grid are called plus isolation (PIS) patterns, and they are error-prone. Recently, we introduced lexicographically-ordered constrained (LOCO) codes, namely optimal plus LOCO (OP-LOCO) codes, with minimal redundancy that prevent these patterns from being written in a TDMR device. However, in the high-density regime or the low-energy regime (as the device ages), additional error-prone patterns emerge, specifically data patterns where a bit is surrounded by complementary bits at only three positions with Manhattan distance 1, and we call them incomplete plus isolation (IPIS) patterns. In this paper, we present capacity-achieving codes that forbid both PIS and IPIS patterns in TDMR systems with wide read heads. Because of their shape, we collectively call the PIS and IPIS patterns rotated T isolation (RTIS) patterns, and we call the new codes optimal T LOCO (OT-LOCO) codes. We analyze OT-LOCO codes and derive their simple encoding-decoding rule that allows reconfigurability. We also present a novel bridging idea for these codes to further increase the rate. Our simulation results demonstrate that OT-LOCO codes not only remarkably outperform OP-LOCO codes, but also entirely eliminate media noise effects, resulting from error-prone data patterns, at practical TD densities in the range [0.6,0.8) with high rates in the range [0.81,0.83]. At the TD density of 0.8, the OT-LOCO code of rate 0.8267 achieves a frame error rate (bit error rate) performance gain of about 1.15 orders (1.23 orders) of magnitude for all TDMR down (horizontal) tracks compared with the uncoded setting. To further preserve the storage capacity, we suggest using OP-LOCO codes, which have higher rates than OT-LOCO codes, early in the device lifetime, then employing the reconfiguration property to switch to OT-LOCO codes later in the device lifetime. While the point of reconfiguration on the density/energy axis is decided manually at the moment, the next step is to use machine learning to make that decision based on the TDMR device status. Moreover, we introduce another coding scheme to remove RTIS patterns in TDMR systems which offers lower complexity, lower error propagation, and track separation, at the expense of a limited rate loss.
Iven Guzel, Dogukan Özbayrak, A. Robert Calderbank, Ahmed H. Hareedy
IEEE Trans. Inf. Theory1