Pattern-dependent noise prediction in signal-dependent noise

TLDR

The paper discusses maximum and near‑maximum likelihood sequence detectors, soft‑output detectors, and soft decision feedback in signal‑dependent noise. The authors propose a noise‑predictive maximum‑likelihood detector using an expanded trellis with branch‑specific pattern‑dependent predictor taps and error variances, and evaluate low‑complexity variants on a positional‑jitter/width‑variation transition‑noise model. The study demonstrates that a linear‑prediction based branch metric yields the optimal expression for signal‑dependent Markov noise, and that a low‑complexity pattern‑dependent noise‑prediction detector achieves a significant SNR gain over the extended class‑4 partial‑response maximum‑likelihood detector when medium noise dominates.

Abstract

Maximum and near-maximum likelihood sequence detectors in signal-dependent noise are discussed. It is shown that the linear prediction viewpoint allows a very simple derivation of the branch metric expression that has previously been shown as optimum for signal-dependent Markov noise. The resulting detector architecture is viewed as a noise predictive maximum likelihood detector that operates on an expanded trellis and relies on computation of branch-specific, pattern-dependent noise predictor taps and predictor error variances. Comparison is made on the performance of various low-complexity structures using the positional-jitter/width-variation model for transition noise. It is shown that when medium noise dominates, a reasonably low complexity detector that incorporates pattern-dependent noise prediction achieves a significant signal-to-noise ratio gain relative to the extended class 4 partial response maximum likelihood detector. Soft-output detectors as well as the use of soft decision feedback are discussed in the context of signal-dependent noise.