Apparatus and method for breaking trapping sets
An error correction data processing apparatus includes a noise predictive calibration circuit operable to calibrate a first set of filter coefficients based on a first data set and a second set of filter coefficients based on a second data set, and includes a first noise predictive detector operable to receive the first set of filter coefficients. The apparatus further includes a decoder operable to perform a first global iteration with the first noise predictive detector and determine a violation check count value, and a second noise predictive detector operable to receive the second set of filter coefficients if the violation check count value is less than a predetermined value or receive the first set of filter coefficients if the violation check count value is greater than the predetermined value.
1. An error correction data processing apparatus comprising:
a noise predictive calibration circuit configured to calibrate a first set of filter coefficients based on a first data set and a second set of filter coefficients based on a second data set;
a first noise predictive detector configured to receive the first set of filter coefficients;
a decoder configured to perform a first global iteration with the first noise predictive detector and determine a violation check count value; and
a second noise predictive detector configured to receive the second set of filter coefficients if the violation check count value is less than a predetermined value or receive the first set of filter coefficients if the violation check count value is greater than the predetermined value.
2. The apparatus of claim 1 , wherein the decoder and the second noise predictive detector are configured to perform a second and above global iterations based on the second set of filter coefficients.
3. The apparatus of claim 1 , wherein the second detector is modified by the second set of filter coefficients during detection in the second and above global iterations between the second noise predictive detector and the decoder if the violation check count value is less than the predetermined value.
4. The apparatus of claim 1 further comprising:
a first buffer configured to store the first set of filter coefficients;
a second buffer configured to store the second set of coefficients; and
a multiplexer configured to switch between loading the first set of filter coefficients or the second set of filter coefficients to the second noise predictive detector.
5. The apparatus of claim 4 further comprising a comparator configured to trigger the multiplexer to switch between loading either the first set of filter coefficients or the second set of filter coefficients based on the violation check count value and the predetermined value.
6. The apparatus of claim 1 further comprising a loading circuit configured to load filter coefficients into the first noise predictive detector and into the second noise predictive detector.
7. The apparatus of claim 1 , wherein the first noise predictive detector and the second noise predictive detector each comprise a soft output Viterbi algorithm detector (SOVA), or each comprise a maximum a posteriori (MAP) algorithm detector.
8. The apparatus of claim 1 , wherein the decoder comprises a Low Density Parity Check (LDPC) algorithm.
9. The apparatus of claim 1 , wherein the noise predictive calibration circuit applies a Least Mean Squares (LMS) algorithm to calibrate the first set of filter coefficients and the second set of filter coefficients.
10. The apparatus of claim 1 wherein the violation check count value represents a trapping set.
11. The apparatus of claim 1 wherein the predetermined value represents a type of error event.
12. The apparatus of claim 1 , wherein at least a portion of the data processing apparatus is fabricated in at least one integrated circuit.
13. A method for improving error correction in a data processing system, the method comprising steps of:
calibrating a first set of filter coefficients based on a first data set and calibrating a second set of filter coefficients based on a second data set;
receiving, by a first noise predictive detector, the first set of filter coefficients;
performing a first global iteration with the first noise predictive detector and a decoder and determining a violation check count value; and
receiving, by a second noise predictive detector, the second set of filter coefficients if the violation check count value is less than a prescribed value or the first set of filter coefficients if the violation check count value is greater than the prescribed value.
14. The method of claim 13 , further comprising performing a second and above global iterations based on the second set of filter coefficients between the decoder and the second noise predictive detector.
15. The method of claim 13 , further comprising modifying the second noise predictive detector with the second set of filter coefficients during detection in the second and above global iterations between the second noise predictive detector and the decoder if the violation check count value is less than the prescribed value.
16. The method of claim 13 , further comprising:
storing in a first buffer the first set of filter coefficients;
storing in a second buffer the second set of coefficients; and
switching between loading the first set of filter coefficients or the second set of filter coefficients to the second noise predictive detector.
17. The method of claim 16 , further comprising loading one of the first set of filter coefficients and the second set of filter coefficients as a function of the violation check count value and the prescribed value.
18. The method of claim 13 , further comprising applying, by both the first noise predictive detector and the second predictive detector, a soft output Viterbi algorithm (SOVA) or applying a maximum a posteriori (MAP) algorithm.
19. The method of claim 13 , further comprising applying a Low Density Parity Check (LDPC) by the decoder.
20. The method of claim 13 , wherein the calibrating step further comprises applying a Least Mean Squares (LMS) algorithm.
21. The method of claim 13 , wherein the violation check count value represents a trapping set.
22. The method of claim 13 , wherein the prescribed value represents a type of error event.
23. A data processing system including first and second noise predictive detectors, the data processing system comprising:
memory; and
at least one processor coupled with the memory and operative: to obtain at least first and second data sets; to calibrate a first set of filter coefficients based on the first data set and to calibrate a second set of filter coefficients based on the second data set; to receive, by the first noise predictive detector, the first set of filter coefficients; to perform a first global iteration with the first noise predictive detector and a decoder and determining a violation check count value; and to receive, by the second noise predictive detector, the second set of filter coefficients if the violation check count value is less than a prescribed value or the first set of filter coefficients if the violation check count value is greater than the prescribed value.
24. An article of manufacture comprising a computer program product, said computer program product in turn comprising a non-transitory computer readable storage medium storing in a non-transitory manner executable program instructions which, when executed, implement the steps of a method according to claim 13 .