Minimum-Bit-Error Rate Tuning for PDNP Detection

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%.