Noise-predictive maximum-likelihood detection

Although advances in head and media technologies have historically been the driving forces behind areal recording density growth, digital signal processing and coding were eventually recognized as cost-efficient techniques for enabling additional substantial increases in areal density while preserving the high operation reliability of [disk drive] (HDD) systems.

In past three decades, several digital signal-processing and coding techniques were introduced into HDDs to improve the drive error-rate performance for operation at ever increasing areal densities as well as to reduce manufacturing and servicing costs. In the early 1990s, partial-response class-4 (PR4) signal shaping in conjunction with maximum-likelihood sequence detection, eventually known as the [[1]] technique, replaced the peak detection systems that employed run-length-limited (RLL) (d,k) -constrained coding. This development also paved the way for future applications of advanced coding and signal-processing techniques in magnetic data storage.

Noise-Predictive Maximum-Likelihood (NPML) is an advanced digital signal-processing method suitable for magnetic data storage systems that operate at high linear recording densities. It is used for reliably retrieving the data recorded in the magnetic medium since data are read back as a weak and noisy analog signal by the read head. Because NPML aims at minimizing the influence of noise in the detection process it allows recording at higher areal densities than other detection schemes, such as Peak Detection, Partial-Response Maximum Likelihood (PRML), and Extended Partial-Response Maximum Likelihood (EPRML) detection.

In general, NPML refers to a family of sequence-estimation data detectors, which arise by imbedding a noise prediction/whitening process into the branch metric computation of the Viterbi algorithm, which is a well known data detection technique for communication channels that exhibit intersymbol interference (ISI) with finite memory.

Reliable operation of the prediction/whitening process is in general achieved by using hypothesized decisions associated with the branches of the trellis on which the Viterbi algorithm operates as well as tentative decisions corresponding to the path memory associated with each trellis state. The NPML detectors can thus be viewed as a family of reduced-state sequence-estimation detectors offering a range of implementation complexities, where complexity is essentially governed by the number of detector states which is equal to 2^k, 0 ≤ K ≤ M, with M denoting the maximum number of controlled ISI terms introduced by the combination of a partial-response shaping equalizer and the noise predictor. By judiciously choosing the parameter K , practical NPML detectors can be devised for the magnetic recording channel that provide a substantial performance improvement over PRML and EPRML detectors in terms of error rate and/or linear recording density.

NPML detection was first described in 1996|journal=Proc. IEEE Intl. Conf. on Commun|year=1996|pages=556-560}}</ref> and eventually found wide application in the read channel design of HDDs. The “noise predictive” concept was later extended to handle not only autoregressive (AR) noise processes but also autoregressive moving-average (ARMA) stationary noise processes. The concept was also extended to include a variety of non-stationary noise sources, such as head, transition jitter and media noise; it was applied with great success to the design of various post-processing schemes for further improvement of the error rate performance. Today noise prediction is used as an integral part of the metric computation in a wide variety of iterative detection/decoding schemes