Minimum-Bit-Error Rate Tuning for PDNP Detection

• Published in: IEEE transactions on magnetics Vol. 57; no. 3; pp. 1 - 6
• Main Authors: Shi, Shanwei, Barry, John R.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-03-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Bit error rate; Data-dependent noise prediction (DDNP); Detectors; Equalizers; Magnetic recording; Magnetism; Measurement; minimum-bit-error rate (MBER); multiple-input multiple-output (MIMO); Noise; Noise prediction; Parameters; Prediction algorithms; Probability density function; Sensors; Two dimensional displays; two-dimensional magnetic recording (TDMR); Viterbi algorithm
• DOI: 10.1109/TMAG.2020.3026979
• EISSN: 1941-0069
• ISSN: 0018-9464

A dominant impediment in magnetic recording is pattern-dependent media noise, and its impact will only grow more severe as areal densities increase. A widely used strategy for mitigating media noise in a trellis-based detector is pattern-dependent noise prediction (PDNP); in this approach, each bit pattern (which determines a trellis branch) will have its own set of branch metric parameters (including the signal levels, noise predictor coefficients, and residual variances). Because the number of states grows exponentially with the number of tracks being detected, a multitrack detector has far more parameters than a single-track detector. In this article, we propose the adaptive minimum-bit-error rate (AMBER) algorithm for adapting these pattern-dependent multitrack detector parameters with the aim of minimizing BER. Numerical results for a 2-D-PDNP multitrack detector based on a quasi-micromagnetic simulated channel show that, when compared to a conventional MMSE criterion, the AMBER algorithm decreases the BER by 17%.