Method for reducing data overhead in PRML data channel

Details Method for reducing data overhead in PRML data channel Method for reducing data overhead in PRML data channel

1999-06-30

2002-04-02

Hudspeth, David (Department: 2651)

Dynamic magnetic information storage or retrieval

General processing of a digital signal

Data in specific format

C360S065000, C360S046000

Reexamination Certificate

active

06366418

ABSTRACT:

FIELD OF THE INVENTION
This invention relates generally to digital data storage channels and devices. More particularly, the present invention relates to a method for reducing sequence detector pad length within a partial response maximum likelihood data channel.
BACKGROUND OF THE INVENTION
In order to achieve higher recording densities, designers of magnetic recording channels have switched from analog peak detection techniques to sampled data detection techniques. In sampled data detection systems, the readback signal is filtered and sampled at a channel rate of 1/T, where T is the duration of a channel symbol. One such technique employs what is known as a partial response maximum likelihood (PRML) system. When PRML is employed, magnetic transition densities on the recording medium may be increased by as much as 20% to 30% over peak detection recording and playback methods, since PRML more robustly tolerates some transition pulse overlap (intersymbol interference) than can resolved with peak detection techniques. Also, in the process of peak detection, the readback signal is differentiated in order to locate signal zero crossover locations. Differentiation amplifies higher frequencies which contributes additional noise to, and increased errors in, the readback signal. The synchronous sampling process employed in PRML quantizes signal amplitudes at specific intervals throughout each readback transition interval T without requiring determination of zero crossings, thereby eliminating the differentiation step and resultant noise enhancement.
One widespread PRML system uses filters to equalize the readback signal to a partial response class 4 (PR4) signal. The discrete-time transfer function of a PR4 channel is (1−D)
2
, where D represents a unit-time delay operator with unit-time T. In an idealized PR4 channel, a noiseless output is equal to the input signal minus a version of the input signal delayed in time by 2T. In a practical PR4 channel, the output of the noisy partial response channel is sampled at the channel rate and detected using a sequence detector, such as a Viterbi detector. Typically, the Viterbi detector is designed for maximum-likelihood detection of the sampled partial response channel in additive, independent, and identically distributed Gaussian noise with zero mean.
While PR4ML channels have been widely used in magnetic recording and playback systems for data densities at or below two channel symbols per pulse width at half maximum amplitude (PW50/T≦2.0) the PR4 spectnim has satisfactorily matched the magnetic recording channel. However, at normalized data densities above PW50/T=2.0, other partial response models have been discovered to provide a better match to the magnetic recording channel characteristics. These partial response models include EPR4 with a discrete-time transfer function of (1−D)(1+D)
2
or (1+D−D
2
−D
3
) and EEPR4 with a discrete-time transfer function of (1−D)(1+D)
3
or (1+2D−2D
3
−D
4
). Other partial response models are also known, such as NPR having a unit pulse response of e.g. 7+4D−4D
2
−5D
3
−2D
4
.
Once a channel model is selected, a sequence detector may be fashioned. Sequence detectors frequently implement a version of the Viterbi algorithm. Typically the Viterbi detector is designed for maximum likelihood detection of the sampled partial response channel in additive, independent, and identically distributed Gaussian noise with zero mean. The Viterbi algorithm minimizes squared Euclidean distance between the sequence of noisy samples and all possible sequences of idealized noiseless samples in accordance with the particular channel model. The Viterbi algorithm is an iterative process of keeping track of the path with the smallest accumulated metric leading to each state. The metrics of all of the paths leading into a particular state are calculated and compared. Then, the path with the smallest metric is selected as a survivor path and the other pathsare discarded. In this manner all paths which are not part of the minimum metric path are systematically eliminated. The survivor path to each state is stored in a path memory. Given that the path memory is made sufficiently long, all of the selected survivor paths will diverge from a single path within the span of the path memory. The single path from which all the current survivor paths diverge is the minimum metric path. The Viterbi detector then traces back along the path memory to find the convergence state. The input sequence associated with the single minimum metric path then becomes the most-likely symbol output of the Viterbi detector.
A Viterbi detector does not attempt to decide whether a transition has occurred upon receipt of a readback sample or samples taken from a particular transition. Rather, samples are taken from the readback signal and equalized to the target channel model. The Viterbi detector then keeps a running tally of the error between the actual sample sequence and a correct sample sequence, i.e. a sequence that would be expected if the recording medium had been written with a particular sequence of transitions. One way of visualizing the Viterbi detector path memory is by way of a trellis diagram having plural states and plural paths leading from each state to other states. As analog-to-digital samples (y
k
) are fed into one end of the trellis, estimates of previous bits are put out at an opposite end of the trellis. An error metric is determined for each one of plural possible state transition sequences. As more samples come into the Viterbi detector, less probable transition sequences (paths) are eliminated, and by tracing back along the trellis a most likely path emerges as a convergent set of paths and enables a most-likely data decision to be made by the Viterbi detector.
The magnetic recording channel is not an ideal channel. Rather, noise, media defects, non-linear response of the playback element and other distracting influences may result in distortion of or error in the readback<signal. Therefore, error events can, and do, occur. When sequence detection is employed, error events may result in a most likely path being selected by the Viterbi detector which diverges from the correct path. Coding constraints are frequently employed in order to limit burst error lengths, so that the trellis (path memory) can be made with a practical maximum number of states. However, in any sequence detector, such as a Viterbi detector, the trellis will have multiple states and must receive multiple samples before it can reach its decision as to each most likely path, and therefore each most likely binary data value (one or zero) to put out.
Examples of magnetic recording and playback channels employing PRML are found in commonly assigned U.S. Pat. No. 5,521,945 to Knudson, entitled: “Reduced Complexity EPR4 Post-Processor for Sampled Data Detection”; and U.S. Pat. No. 5,844,738 to Behrens et al., entitled: “Synchronous Read Channel Employing a Sequence Detector with Programmable Detector Levels”. A paper by R. Behrens and A. Armstrong, entitled: “An Advanced Read/Write Channel for Magnetic Disk Storage”,
IEEE Twenty
-
Sixth Asilomar Conf. on Signals, Systems
&
Computers
, Vol. 2, pp. 956-960, October 1992, also provides useful background information concerning a number of issues relating to PRML.
In magnetic data storage devices, such as hard disk drives for example, user data is typically stored in blocks or sectors defined within a data track. Tracks may be a single spiral track as in optical recording, or may be a multiplicity of discrete concentric tracks as is the practice in magnetic disk recording. Each data sector typically begins with certain overhead information which may include a synchronization field, an address mark pattern enabling data blocks to be properly framed, a data field of user data bytes and ECC syndrome bytes, and a pad field. Since the sequence detector uses path metrics and multiple states in arriving at each data decision, it has h

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