This paper presents a turbo-detection system consisting of a convolutional neural network (CNN) based equalizer, a Bahl-Cocke-Jelinek-Raviv (BCJR) trellis detector, a CNN-based media noise predictor (MNP), and a low-density parity-check (LDPC) channel decoder for two-dimensional magnetic recording (TDMR). The BCJR detector, CNN MNP, and LDPC decoder iteratively exchange soft information to maximize the areal density (AD) subject to a bit error rate (BER) constraint. Simulation results employing a realistic grain switching probabilistic (GSP) media model show that the proposed system is quite robust to track-misregistration (TMR). Compared to a I-D pattern-dependent noise prediction (PDNP) baseline with soft intertrack interference (ITI) subtraction, the system achieves 0.34% AD gain with read-TMR alone and 0.69% with write- and read-TMR together.
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Joint Turbo Receiver for LDPC-Coded MIMO Systems Based on Semi-definite Relaxation
Semi-definite relaxation (SDR) has demonstrated
the capability of approaching maximum-likelihood (ML) performance.
In this work, we first develop a new SDR-based detector
that exploits forward error correction (FEC) code information
in the detection stage. The joint SDR detector substantially
improves overall receiver performance by generating highly
reliable information to downstream decoder. For further performance
improvement, we integrate the joint SDR detector with
decoder using a feedback link to form an iterative turbo receiver.
Meanwhile, we propose a simplified SDR receiver that solves only
one SDR problem per codeword instead of solving multiple SDR
problems in the iterative turbo processing. This simplification
significantly reduces the complexity of SDR turbo receiver, while
maintaining a similarly superior error performance.
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- Award ID(s):
- 1711823
- NSF-PAR ID:
- 10066207
- Date Published:
- Journal Name:
- IEEE Vehicular Technology Conference
- ISSN:
- 1090-3038
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
