System and method for estimating noise characteristics in the presence of non-stationary noise

• US 6,744,813 B1
• Filed: 04/14/2000
• Issued: 06/01/2004
• Est. Priority Date: 04/16/1999
• Status: Expired due to Fees

• Current Assignee: Summit Technology Systems LP
• Original Assignee: Paradyne Corporation (Dasan Networks Incorporated)
• Inventors: Ko, Ken, Betts, William
• Primary Examiner(s): Chin, Stephen
• Assistant Examiner(s): ODOM, CURTIS B
• Application Number:
  US09/550,299
• Time in Patent Office: 1,509 Days
• Field of Search: 375/224, 375/232, 375/296, 375/147, 375/134, 375/130, 341/200, 702/66, 702/77
• US Class Current: 375/224
• CPC Class Codes: H04B 3/46 Monitoring; Testing
• CPC Class Codes: H04M 3/18 with means for reducing interference or noise; with means for reducing effects due to line faults with means for protecting lines
• CPC Class Codes: H04M 3/30 for subscriber's lines , for the local loop

Abstract

A thresholding noise estimator detects and samples an error signal during a sampling period. When the magnitude of a noise sample, also known as an error sample, is at least equal to a predefined threshold, the power of that error sample is calculated. At the end of the sampling period, the computed powers of the error samples (only those data samples having a magnitude at least equal to the predefined threshold) are averaged. This average power corresponds to an error indicator which quantitatively indicates the power for that portion(s) of an error signal which is at least equal to the threshold. Alternative embodiments provide for magnitude hysteresis, time hysteresis, or both magnitude and time hysteresis in the determination of an error indicator. Also, alternative embodiments provide for adding the power of the peak error sample to the average power in the determination of an error indicator, or, provide for adding an interleaved average peak power to the average power in the determination of an error indicator.

Claims

1. A method for estimating error in an input signal, the method comprising the steps of:

measuring a magnitude of a plurality of data samples from said input signal;

defining as an error sample each one of said plurality of data samples when the magnitude of said data sample is at least equal to a first threshold, such that a plurality of error samples are defined;

calculating a power for each one of said plurality of error samples; and

specifying an error indicator, said error indicator corresponding to an average of said calculated powers, wherein said error indicator represents the power of the error in said input signal.

2. The method of claim 1, wherein said specifying step specifies said error indicator corresponding to the root mean square (RMS) magnitude of the error in said input signal.

3. The method of claim 1, wherein said defining step further includes identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said specifying step specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

4. The method of claim 1, wherein said defining step further comprises the steps of interleaving said plurality of error samples into a predetermined plurality of sample subsets and identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said calculating step further calculates said magnitude for each said peak error sample, and wherein said specifying step specifies said error indicator corresponding to said average power and an average of said magnitudes.

5. The method of claim 1, wherein the step of defining said data sample as said error sample continues for a predetermined period of time after said magnitude of said input signal decreases from at least equal to said first threshold to below said first threshold.

6. The method of claim 5, wherein said defining step further includes identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said specifying step specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

7. The method of claim 5, wherein said defining step further comprises the steps of interleaving said plurality of error samples into a predetermined plurality of sample subsets and identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said calculating step further calculates said magnitude for each said peak error sample, and wherein said specifying step specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

8. The method of claim 1, wherein the step of defining said data sample as said error sample continues for a predetermined number of data samples after said magnitude of said input signal decreases from at least equal to said first threshold to below said first threshold.

9. The method of claim 8, wherein said defining step further includes identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said specifying step specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

10. The method of claim 8, wherein said defining step further comprises the steps of interleaving said plurality of error samples into a predetermined plurality of sample subsets and identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said calculating step further calculates said magnitude for each said peak error sample, and wherein said specifying step specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

11. The method of claim 1, wherein the step of defining said data sample as an error sample continues after said magnitude of said input signal decreases from at least equal to said first threshold to below a second threshold, wherein said second threshold is less than said first threshold.

12. The method of claim 11, wherein said defining step further includes identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said specifying step specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

13. The method of claim 11, wherein said defining step further comprises the steps of interleaving said plurality of error samples into a predetermined plurality of sample subsets and identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said calculating step further calculates said magnitude for each said peak error sample, and wherein said specifying step specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

14. The method of claim 1, wherein the step of defining said data sample as an error sample continues after said magnitude of said input signal decreases from at least equal to said first threshold to below a second threshold, and then for a predetermined period of time after said magnitude of said input signal decreases below said second threshold, and wherein said second threshold is less than said first threshold.

15. The method of claim 14, wherein said defining step further includes identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said specifying step specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

16. The method of claim 14, wherein said defining step further comprises the steps of interleaving said plurality of error samples into a predetermined plurality of sample subsets and identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said calculating step further calculates said magnitude for each said peak error sample, and wherein said specifying step specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

17. The method of claim 1, wherein said defining step defines as an error sample each one of said plurality of data samples when the power of said data sample is at least equal to said first threshold, such that said plurality of error samples are defined.

18. The method of claim 17, wherein said defining step further includes identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said specifying step specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

19. The method of claim 17, wherein said defining step further comprises the steps of interleaving said plurality of error samples into a predetermined plurality of sample subsets and identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said calculating step further calculates said magnitude for each said peak error sample, and wherein said specifying step specifies said error indicator corresponding to said average power and an average of said magnitudes.

20. The method of claim 17, wherein the step of defining said data sample as said error sample continues for a predetermined period of time after said power of said input signal decreases from at least equal to said first threshold to below said first threshold.

21. The method of claim 20, wherein said defining step further includes identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said specifying step specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

22. The method of claim 20, wherein said defining step further comprises the steps of interleaving said plurality of error samples into a predetermined plurality of sample subsets and identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said calculating step further calculates said magnitude for each said peak error sample, and wherein said specifying step specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

23. The method of claim 17, wherein the step of defining said data sample as said error sample continues for a predetermined number of data samples after said power of said input signal decreases from at least equal to said first threshold to below said first threshold.

24. The method of claim 23, wherein said defining step further includes identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said specifying step specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

25. The method of claim 23, wherein said defining step further comprises the steps of interleaving said plurality of error samples into a predetermined plurality of sample subsets and identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said calculating step further calculates said magnitude for each said peak error sample, and wherein said specifying step specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

26. The method of claim 17, wherein the step of defining said data sample as an error sample continues after said power of said input signal decreases from at least equal to said first threshold to below a second threshold, wherein said second threshold is less than said first threshold.

27. The method of claim 26, wherein said defining step further includes identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said specifying step specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

28. The method of claim 26, wherein said defining step further comprises the steps of interleaving said plurality of error samples into a predetermined plurality of sample subsets and identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said calculating step further calculates said magnitude for each said peak error sample, and wherein said specifying step specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

29. The method of claim 17, wherein the step of defining said data sample as an error sample continues after said power of said input signal decreases from at least equal to said first threshold to below a second threshold, and then for a predetermined period of time after said power of said input signal decreases below said second threshold, and wherein said second threshold is less than said first threshold.

30. The method of claim 29, wherein said defining step further includes identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said specifying step specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

31. The method of claim 29, wherein said defining step further comprises the steps of interleaving said plurality of error samples into a predetermined plurality of sample subsets and identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said calculating step further calculates said magnitude for each said peak error sample, and wherein said specifying step specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

68. The method of claim 1, wherein said defining step further includes identifying a peak error sample such that said peak error sample has the greatest power of said plurality of error samples, and wherein said specifying step specifies said error indicator corresponding to said average power and said power of said peak error sample.

69. The method of claim 1, wherein said defining step further comprises the steps of interleaving said plurality of error samples into a predetermined plurality of sample subsets and identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest power of said error samples in each one of said plurality of sample subsets, and wherein said calculating step further calculates said power for each said peak error sample, and wherein said specifying step specifies said error indicator corresponding to said average power and an average of said powers of said peak error samples in each one of said plurality of sample subsets.

32. A system for estimating error in an input signal, comprising:

means for measuring the magnitude of a plurality of data samples from said input signal;

means for defining as an error sample each one of said plurality of data samples when the magnitude of said data sample is at least equal to a first threshold, such that a plurality of error samples are defined;

means for calculating a power for each one of said plurality of error samples; and

means for specifying an error indicator, said error indicator corresponding to an average of said calculated powers, wherein said error indicator represents the power of the error in said input signal.

33. The system of claim 32, wherein said means for specifying specifies said error indicator corresponding to the root mean square (RMS) magnitude of the error in said input signal.

34. The system of claim 32, wherein said means for defining further includes a means for identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said means for specifying specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

35. The system of claim 32, wherein said means for defining further comprises a means for interleaving said plurality of error samples into a predetermined plurality of sample subsets and a means for identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said means for calculating further calculates said magnitude for each said peak error sample, and wherein said means for specifying specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

36. The system of claim 32, wherein the means for defining said data sample as said error sample continues for a predetermined period of time after said magnitude of said input signal decreases from at least equal to said first threshold to below said first threshold.

37. The system of claim 36, wherein said means for defining further includes a means for identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said means for specifying specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

38. The system of claim 36, wherein said means for defining further comprises a means for interleaving said plurality of error samples into a predetermined plurality of sample subsets and a means for identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said means for calculating further calculates said magnitude for each said peak error sample, and wherein said means for specifying specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

39. The system of claim 32, wherein said means for defining said data sample as said error sample continues for a predetermined number of data samples after said magnitude of said input signal decreases from at least equal to said first threshold to below said first threshold.

40. The system of claim 39, wherein said means for defining further includes a means for identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said means for specifying specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

41. The system of claim 39, wherein said means for defining further comprises a means for interleaving said plurality of error samples into a predetermined plurality of sample subsets and a means for identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said means for calculating further calculates said magnitude for each said peak error sample, and wherein said means for specifying specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

42. The system of claim 32, wherein said means for defining said data sample as an error sample continues after said magnitude of said input signal decreases from at least equal to said first threshold to below a second threshold, wherein said second threshold is less than said first threshold.

43. The system of claim 42, wherein said means for defining further includes a means for identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said means for specifying specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

44. The system of claim 42, wherein said means for defining further comprises a means for interleaving said plurality of error samples into a predetermined plurality of sample subsets and a means for identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said means for calculating further calculates said magnitude for each said peak error sample, and wherein said means for specifying specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

45. The system of claim 32, wherein said means for defining said data sample as an error sample continues after said magnitude of said input signal decreases from at least equal to said first threshold to below a second threshold, and then for a predetermined period of time after said magnitude of said input signal decreases below said second threshold, and wherein said second threshold is less than said first threshold.

46. The system of claim 45, wherein said means for defining further includes a means for identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said means for specifying specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

47. The system of claim 45, wherein said means for defining further comprises a means for interleaving said plurality of error samples into a predetermined plurality of sample subsets and a means for identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said means for calculating further calculates said magnitude for each said peak error sample, and wherein said means for specifying specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

48. The system of claim 32, wherein said means for defining defines as an error sample each one of said plurality of data samples when the power of said data sample is at least equal to said first threshold, such that said plurality of error samples are defined.

49. The system of claim 48, wherein said means for defining further includes a means for identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said means for specifying specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

50. The system of claim 48, wherein said means for defining further comprises the a means for interleaving said plurality of error samples into a predetermined plurality of sample subsets and a means for identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said means for calculating further calculates said magnitude for each said peak error sample, and wherein said means for specifying specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

51. The system of claim 48, wherein the means for defining said data sample as said error sample continues for a predetermined period of time after said power of said input signal decreases from at least equal to said first threshold to below said first threshold.

52. The system of claim 51, wherein said means for defining further includes a means for identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said means for specifying specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

53. The system of claim 51, wherein said means for defining further comprises a means for interleaving said plurality of error samples into a predetermined plurality of sample subsets and a means for identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said means for calculating further calculates said magnitude for each said peak error sample, and wherein said means for specifying specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

54. The system of claim 48, wherein said means for defining said data sample as said error sample continues for a predetermined number of data samples after said power of said input signal decreases from at least equal to said first threshold to below said first threshold.

55. The system of claim 54, wherein said means for defining further includes a means for identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said means for specifying specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

56. The system of claim 54, wherein said means for defining further comprises a means for interleaving said plurality of error samples into a predetermined plurality of sample subsets and a means for identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said means for calculating further calculates said magnitude for each said peak error sample, and wherein said means for specifying specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

57. The system of claim 48, wherein said means for defining said data sample as an error sample continues after said power of said input signal decreases from at least equal to said first threshold to below a second threshold, wherein said second threshold is less than said first threshold.

58. The system of claim 57, wherein said means for defining further includes a means for identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said means for specifying specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

59. The system of claim 57, wherein said means for defining further comprises a means for interleaving said plurality of error samples into a predetermined plurality of sample subsets and a means for identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said means for calculating further calculates said magnitude for each said peak error sample, and wherein said means for specifying specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

60. The system of claim 48, wherein said means for defining said data sample as an error sample continues after said power of said input signal decreases from at least equal to said first threshold to below a second threshold, and then for a predetermined period of time after said power of said input signal decreases below said second threshold, and wherein said second threshold is less than said first threshold.

61. The system of claim 60, wherein said means for defining further includes a means for identifying a peak error sample such that said peak error sample has the greatest magnitude of said plurality of error samples, and wherein said means for specifying specifies said error indicator corresponding to said average power and said magnitude of said peak error sample.

62. The system of claim 60, wherein said means for defining further comprises a means for interleaving said plurality of error samples into a predetermined plurality of sample subsets and a means for identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest magnitude of said error samples in each one of said plurality of sample subsets, and wherein said means for calculating further calculates said magnitude for each said peak error sample, and wherein said means for specifying specifies said error indicator corresponding to said average power and an average of said peak magnitudes.

70. The system of claim 32, wherein said means for defining further includes identifying a peak error sample such that said peak error sample has the greatest power of said plurality of error samples, and wherein said specifying step specifies said error indicator corresponding to said average power and said power of said peak error sample.

71. The system of claim 32, wherein said means for defining further comprises a means for interleaving said plurality of error samples into a predetermined plurality of sample subsets and a means for identifying a peak error sample for each one of said plurality of sample subsets such that said peak error sample has the greatest power of said error samples in each one of said plurality of sample subsets, and wherein said means for calculating further calculates said power for each said peak error sample, and wherein said means for specifying specifies said error indicator corresponding to said average power and an average of said powers of said peak error samples in each one of said plurality of sample subsets.

63. A computer readable medium having a program for storing a series of instructions for estimating error in an input signal, the program for performing at least the following:

measuring the magnitude of a plurality of data samples from said input signal;

defining as an error sample each one of said plurality of data samples when the magnitude of said data sample is at least equal to a first threshold, such that a plurality of error samples are defined;

calculating a power for each one of said plurality of error samples; and

specifying an error indicator, said error indicator corresponding to an average of said calculated powers, wherein said error indicator represents the power of the error in said input signal.

64. A computer readable medium of claim 63, wherein the step of defining said data sample as said error sample continues for a predetermined period of time after a magnitude of said input signal decreases from at least equal to said first threshold to below said first threshold.

65. A computer readable medium of claim 63, wherein the step of defining said data sample as said error sample continues for a predetermined number of data samples after a magnitude of said input signal decreases from at least equal to said first threshold to below said first threshold.

66. A computer readable medium of claim 63, wherein the step of defining said data sample as an error sample continues after said magnitude of said input signal decreases from at least equal to said first threshold to below a second threshold, wherein said second threshold is less than said first threshold.

67. A computer readable medium of claim 63, wherein the series of instructions defines as an error sample each one of said plurality of data samples when the power of said data sample is at least equal to said first threshold, such that said plurality of error samples are defined.