Enhancing the ethernet FEC state machine to strengthen correlator performance

Abstract

The present invention discloses devices and methods for improving data correlation using a multiple-correlation state-machine, the method including the steps of: (a) pre-processing a data frame having a plurality of symbol sets, wherein each symbol set demarks a respective frame field of the frame, to provide a threshold-compared hamming-distance indicator; (b) comparing the threshold-compared hamming-distance indicator with at least one multiple-correlation threshold to provide a threshold-compared multiple-correlation indicator; and (c) combining the threshold-compared hamming-distance indicator and the threshold-compared multiple-correlation indicator to determine a match/no-match comparison indicative of the respective frame field. In some embodiments, the step of combining includes forming a logical-AND of the threshold-compared hamming-distance indicator and the threshold-compared multiple-correlation indicator. Preferably, the method further includes the step of: (d) prior to the step of combining, comparing a BER of the frame to a BER threshold. Also disclosed is a weighted-correlation method for improving data correlation.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is herein described, by way of example only, with reference to the accompanying drawings, wherein:

FIG. 1 shows the format of the field blocks of a coded packet frame, according to the present invention;

FIG. 2 is a simplified schematic block diagram of the data flow in a correlator state machine, according to the present invention;

FIG. 3 is a high-level flowchart of the processing steps in the double-correlation method, according to the present invention;

FIG. 4 is a high-level flowchart of the processing steps in the weighted-correlation method, according to the present invention;

FIG. 5 is a high-level flowchart of typical pre-processing steps in prior-art methods;

FIG. 6 is a schematic flowchart of the processing steps in a multiple-correlation method, according to the present invention;

FIG. 7 is a schematic flowchart of the processing steps in the weighted-correlation method, according to the present invention.

Claims

1. A method for improving data correlation using a multiple-correlation state-machine, the method comprising the steps of: (a) pre-processing a data frame having a plurality of symbol sets; wherein each said symbol set demarks a respective frame field of said frame, to provide a threshold-compared hamming-distance indicator;(b) comparing said threshold-compared hamming-distance indicator with at least one multiple-correlation threshold to provide a threshold-compared multiple-correlation indicator; and(c) combining said threshold-compared hamming-distance indicator and said threshold-compared multiple-correlation indicator to determine a match/no-match comparison indicative of a field of said frame.

2. The method of claim 1, wherein said step of pre-processing includes the steps of: (i) checking said frame using at least two correlators matched to at least two said symbol sets to provide a correlation sequence;(ii) analyzing said correlation sequence to provide a hamming-distance indicator; and(iii) comparing said hamming-distance indicator with a hamming-distance threshold to provide said threshold-compared hamming-distance indicator.

3. The method of claim 2, wherein each said frame includes a first symbol-set field delimiting a beginning of a preamble field of said each frame.

4. The method of claim 3, wherein each said frame includes a second symbol-set field delimiting an end a parity-bytes field of said each frame.

5. The method of claim 4, wherein each said frame includes a third symbol-set field delimiting an end of said each frame.

6. The method of claim 5, wherein said at least two correlators are based on said second symbol-set field and said third symbol-set field.

7. The method of claim 1, the method further comprising the step of: (d) prior to said step of combining, comparing a BER of said frame to a BER threshold.

8. The method of claim 1, wherein said step of combining includes forming a logical-AND of said threshold-compared hamming-distance indicator and said threshold-compared multiple-correlation indicator.

9. The method of claim 1, wherein said data frame is an FEC-protected data frame.

10. The method of claim 1, the method further comprising the step of. (d) prior to performing step (c), iterating step (b), wherein said comparing includes comparing each indicator, of a plurality of indicators, with each said multiple-correlation threshold to provide a threshold-compared multiple-correlation indicator.

11. A method for improving data correlation using weighted correlation, the method comprising the steps of: (a) pre-processing a data frame having a plurality of symbol sets, wherein each said symbol set demarks a respective frame field of said frame, to provide a threshold-compared hamming-distance indicator;(b) weighting said threshold-compared hamming-distance indicator to provide a weighted hamming-distance indicator according to a parameter set;(c) comparing said weighted hamming-distance indicator with a weighted-correlation threshold to provide a threshold-compared weighted-correlation indicator; and(d) combining said weighted hamming-distance indicator and said threshold-compared weighted-correlation indicator to determine a match/no-match comparison indicative of a field of said frame.

12. The method of claim 11, wherein said step of pre-processing includes the steps of: (i) checking said frame using at least one correlator matched to at least one said symbol set to provide a correlation sequence;(ii) analyzing said correlation sequence to provide a hamming-distance indicator; and(iii) comparing said hamming-distance indicator with a hamming-distance threshold to provide said threshold-compared hamming-distance indicator.

13. The method of claim 12, wherein each said frame includes a first symbol-set field delimiting a beginning of a preamble field of said each frame.

14. The method of claim 13, wherein each said frame includes with a second symbol-set field delimiting an end a parity-bytes field of said each frame.

15. The method of claim 14, wherein each said frame includes a third symbol-set field delimiting an end of said each frame.

16. The method of claim 15, wherein said at least two correlators are based on said second symbol-set field and said third symbol-set field.

17. The method of claim 11, wherein said step of weighting is based on bits having respective said symbol sets distanced one bit from a correlated delimiter of said respective frame field.

18. The method of claim 17, wherein said step of weighting is dependent on a BER of said frame.

19. The method of claim 17, the method further comprising the step of: (e) prior to said step of combining, comparing a BER of said frame to a BER threshold.

20. The method of claim 11, wherein said step of combining includes forming a logical-AND of said weighted hamming-distance indicator and said threshold-compared weighted-correlation indicator.

21. The method of claim 11, wherein said data frame is an FEC-protected data frame.

22. A device configured to operate according to the method of claim 1.

23. A device configured to operate according to the method of claim 11.