Methods and apparatuses for detecting impulse noise in a multi-carrier communication system

• US 20060067388A1
• Filed: 09/30/2004
• Published: 03/30/2006
• Est. Priority Date: 09/30/2004
• Status: Abandoned Application

• Current Assignee: 2Wire, Inc. (CommScope Holding Company, Inc.)
• Original Assignee: 2Wire, Inc. (CommScope Holding Company, Inc.)
• Inventors: Sedarat, Hossein
• Application Number:
  US10/956,201
• Publication Number:
  US 20060067388A1
• Time in Patent Office: Days
• US Class Current: 375/219
• CPC Class Codes: H04L 1/20 using signal quality detector
• CPC Class Codes: H04L 27/2647 Arrangements specific to the receiver only equalisation H04L27/01
• CPC Class Codes: H04L 27/34 Amplitude- and phase-modulated carrier systems, e.g. quadrature-amplitude modulated carrier systems

Abstract

Embodiments of an apparatus, system, and method are described for a multi-carrier communication system that detects for impulse noise present on a transmission medium. Values of peak error samples may be measured to determine an approximate magnitude of the average peak error samples present on a transmission medium. An average error value of all of the error samples may be measured to determine a standard deviation of a Gaussian distribution of background noise. An amount of peak error samples may be compared to a threshold value that is based upon a standard deviation derived from the background noise to determine if impulse noise is present on a particular tone.

Claims

1. A method, comprising:

detecting for impulse noise present in a multiple tone system;

measuring values of peak error samples to determine an approximate magnitude of an average of peak error samples present on a transmission medium;

measuring an average error value of all error samples to determine a standard deviation of a Gaussian distribution of background noise; and

comparing an amount of peak error samples to a threshold value that is based upon a standard deviation derived from background noise to determine if impulse noise is present on a tone.

2. The method of claim 1, wherein comparing an amount of peak error samples further comprises:

comparing a frequency of error samples with a magnitude greater than the threshold value to determine if impulse noise is present.

3. The method of claim 2, wherein comparing the frequency further comprises:

counting a number of error samples with the magnitude greater than the threshold value that is based upon the standard deviation derived from the background noise.

calculating the frequency of these error samples with the magnitude greater than the threshold by dividing the number of error samples with the magnitude greater than the threshold over a total number of error samples detected; and

determining if the frequency of these error samples with the magnitude greater than the threshold is higher than a set point, then impulse noise is determined to be present on the first tone.

4. The method of claim 1, wherein comparing an amount of peak error samples further comprises:

comparing a magnitude of a Gaussian distribution of peak error samples to a magnitude of a Gaussian distribution of background noise error samples; and

determining if a ratio of peak error samples to background noise error samples is higher than a set point, then impulse noise is determined to be present on the first tone.

5. The method of claim 1, wherein comparing an amount of peak error samples further comprises:

comparing a distance between a Gaussian distribution of peak error samples to a threshold based on the standard deviation of the Gaussian distribution of background noise error samples; and

determining if the distance is high enough and the Gaussian distribution of peak error samples has a magnitude greater than a set point, then impulse noise is determined to be present.

6. The method of claim 1, further comprising:

determining values for the amount of peak error samples and the standard deviation of the Gaussian distribution of background noise error samples using a Gaussian-mixture model with a Maximum-Likelihood algorithm and an Expectation-Maximization algorithm.

7. The method of claim 1, further comprising:

using a Gaussian-mixture model with a Maximum-Likelihood algorithm and a set of assumptions to yield values for the Maximum-Likelihood algorithm to determine the values for the amount of peak error samples and the standard deviation of the Gaussian distribution of background noise error samples.

8. The method of claim 1, further comprising:

using a set of assumptions, including assuming that an impulse noise activation frequency is much higher than a target error rate and an average magnitude of peak error samples is at least two times greater than the standard deviation of the Gaussian distribution of background noise error samples, to determine values for the amount of peak error samples and the standard deviation of the Gaussian distribution of background noise error samples.

9. The method of claim 1, further comprising:

determining if the presence of impulse noise is detected on two or more tones transmitted on a same transmission medium, then declaring that an impulse noise source is associated with the transmission medium.

10. A machine readable medium storing instructions to cause the machine to perform the method of claim 1.

11. A machine readable medium storing instructions to cause the machine to perform the method of claim 8.

12. An apparatus, comprising:

means for detecting for impulse noise present in a multiple tone system;

means for measuring values of peak error samples to determine an approximate magnitude of an average of peak error samples present on a transmission medium;

means for measuring an average error value of all error samples to determine a standard deviation of a Gaussian distribution of background noise; and

means for comparing an amount of peak error samples to a threshold value that is based upon a standard deviation derived from background noise to determine if impulse noise is present on a first tone.

13. The apparatus of claim 12, further comprising:

means for comparing a magnitude of a Gaussian distribution of peak error samples to a magnitude of a Gaussian distribution of background noise error samples.

14. The apparatus of claim 12, further comprising:

means for determining values for the amount of peak error samples and the standard deviation of the Gaussian distribution of background noise error samples using a Gaussian-mixture model with a Maximum-Likelihood algorithm and an Expectation-Maximization algorithm.

15. The apparatus of claim 12, further comprising:

means for determining values for the amount of peak error samples and the standard deviation of the Gaussian distribution of background noise error samples using a Gaussian-mixture model with a Maximum-Likelihood algorithm and a set of assumptions to yield values for the Maximum-Likelihood algorithm.

16. The apparatus of claim 12, further comprising:

means for determining values for the amount of peak error samples and the standard deviation of the Gaussian distribution of background noise error samples using a set of assumptions, including assuming that an impulse noise activation frequency is much higher than a target error rate and an average magnitude of peak error samples is at least two times greater than the standard deviation of the Gaussian distribution of background noise error samples.

17. The apparatus of claim 12, further comprising:

means for determining if the presence of impulse noise is detected on two or more tones transmitted on a same transmission medium, then declaring that an impulse noise source is associated with the transmission medium.

18. A transmitter-receiver device, comprising:

a transmitter portion; and

a receiver portion having an impulse noise detector configured to detect an error difference between an amplitude of each transmitted data point and an expected amplitude for each data point in order to detect for the presence of impulse noise;

wherein the error difference for each transmitted data point is an error sample.

19. The transmitter-receiver device of claim 18, wherein the impulse noise detector is configured to calculate a power of the error samples on each tone and sets a magnitude threshold for the error samples for each tone based upon a standard deviation for average power of Gaussian distribution of error samples of noise on that tone.

20. The transmitter-receiver device of claim 18, wherein the impulse noise detector is configured to count a number of error samples with a magnitude greater than the magnitude threshold value that is based upon the standard deviation derived from the background noise.

21. The transmitter-receiver device of claim 20, wherein the impulse noise detector is configured to calculate a frequency of the error samples with the magnitude greater than the threshold by dividing the number of error samples with the magnitude greater than the threshold over a total number of error samples detected.

22. The transmitter-receiver device of claim 21, wherein the impulse noise detector is configured to determine if he frequency of error samples with the magnitude greater than the threshold is higher than a set point, then an impulse noise is determined to be present on the first tone.

23. The transmitter-receiver device of claim 18, wherein the impulse noise detector is configured to determine if a number of tones having impulse noise present is greater than a tone count threshold, then declare that an impulse noise source is associated with a transmission medium.