2021
DOI: 10.1016/j.dcan.2020.02.003
|View full text |Cite
|
Sign up to set email alerts
|

An iterative ITI cancellation method for multi-head multi-track bit-patterned magnetic recording systems

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
3

Relationship

2
1

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 9 publications
0
3
0
Order By: Relevance
“…Furthermore, we found that the proposed system is also robust to TMR and media noise, if compared to both the DTDH system using a hard ITI suppression technique and the conventional system. Eventually, it should be pointed out that the proposed technique can also be applied for the m-track m-head (m ≥ 3) detection in BPMR systems, as presented in [26]. .…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, we found that the proposed system is also robust to TMR and media noise, if compared to both the DTDH system using a hard ITI suppression technique and the conventional system. Eventually, it should be pointed out that the proposed technique can also be applied for the m-track m-head (m ≥ 3) detection in BPMR systems, as presented in [26]. .…”
Section: Discussionmentioning
confidence: 99%
“…[4]. Many research works have been conducted to handle the 2D interference, e.g., multi-head multi-track detection [5], neural network-based equalizer and detector [6,7], turbo equalization [5,8], equalizer and target design [9], modified detector [4], arrangement of magnetic island [10,11] and modulation code [12,13].…”
Section: Introductionmentioning
confidence: 99%
“…In the detection of this GPR target, ITI information is supplied to convert the 2D detection into 1D detection with the same performance and reduced complexity. The proposed scheme extracts the ITI information supplied from multiple GPR targets instead of exploiting multi-head multitrack to subtract the ITI, as in [12,[26][27][28][29]. The proposed model exploits multiple layers of GPR targets to estimate each layer of original data.…”
Section: Introductionmentioning
confidence: 99%