DSL system estimation and parameter recommendation

• US 7,302,379 B2
• Filed: 04/02/2004
• Issued: 11/27/2007
• Est. Priority Date: 12/07/2003
• Status: Active Grant

• Current Assignee: Assia Spe LLC
• Original Assignee: Adaptive Spectrum and Signal Alignment, Incorporated
• Inventors: Cioffi, John M., Rhee, Wonjong
• Primary Examiner(s): Rodriguez; Paul
• Assistant Examiner(s): SHARON, AYAL I
• Application Number:
  US10/817,128
• Publication Number:
  US 20050123027A1
• Time in Patent Office: 1,334 Days
• Field of Search: 703/2, 703/20, 703/21, 379/22.04, 379/22.03, 379/23, 379/24, 370/241, 370/251
• US Class Current: 703/20
• CPC Class Codes: H04B 3/32 Reducing cross-talk, e.g. by compensating
• CPC Class Codes: H04L 1/0002 by adapting the transmission rate
• CPC Class Codes: H04L 1/0019 in which mode-switching is based on a statistical approach
• CPC Class Codes: H04L 12/2856 Access arrangements, e.g. Internet access asynchronous transfer mode networks H04L12/5601; broadband local area networks H04L12/2801; optical access or distribution networks H04Q11/0067; access to open networks H04L12/5691; digital subscriber line end-user equipment and bit-level processing of data on a PSTN-based network H04M11/00; home network gateways H04L12/2834; wireless access networks H04W
• CPC Class Codes: H04L 12/2874 Processing of data for distribution to the subscribers
• CPC Class Codes: H04L 12/2898 Subscriber equipments DSL modems H04M11/062; cable modems H04L12/2801
• CPC Class Codes: H04L 12/6418 Hybrid transport
• CPC Class Codes: H04L 2012/6478 Digital subscriber line, e.g. DSL, ADSL, HDSL, XDSL, VDSL
• CPC Class Codes: H04L 41/0806 for initial configuration or provisioning, e.g. plug-and-play
• CPC Class Codes: H04L 41/0853 by actively collecting configuration information or by backing up configuration information
• CPC Class Codes: H04L 41/145 involving simulating, designing, planning or modelling of a network
• CPC Class Codes: H04L 43/00 Arrangements for monitoring or testing data switching networks
• CPC Class Codes: H04L 43/0829 Packet loss
• CPC Class Codes: H04L 43/0847 Transmission error
• CPC Class Codes: H04L 43/0888 Throughput
• CPC Class Codes: H04L 43/0894 Packet rate
• CPC Class Codes: H04L 43/106 using time related information in packets, e.g. by adding timestamps
• CPC Class Codes: H04L 43/16 Threshold monitoring
• CPC Class Codes: H04L 5/0044 allocation of payload
• CPC Class Codes: H04M 11/062 using different frequency bands for speech and other data
• CPC Class Codes: H04M 3/2209 : for lines also used for data transmission
• CPC Class Codes: H04M 3/30 : for subscriber's lines , for the local loop
• CPC Class Codes: H04M 3/304 : and using xDSL modems xDSL line qualification H04M3/306
• CPC Class Codes: H04M 3/34 : Testing for cross-talk
• CPC Class Codes: Y02D 30/50 : in wire-line communication networks, e.g. low power modes or reduced link rate

Abstract

Estimates of a communication system configuration, such as a DSL system, are based on operational data collected from a network element management system, protocol, users and/or the like. The operational data collected from the system can include performance-characterizing operational data that typically is available in an ADSL system via element-management-system protocols. Generated estimates and/or approximations can be used in evaluating system performance and directly or indirectly dictating/requiring changes or recommending improvements in operation by transmitters and/or other parts of the communication system. Data and/or other information may be collected using “internal” means or may be obtained from system elements and components via email and/or other “external” means. The likelihood of a model'"'"'s accuracy can be based on various data, information and/or indicators of system performance, such as observed normal operational data, test data and/or prompted operational data that shows operating performance based on stimulation signals. One example of such prompted data uses frequency carrier masks to approximate the Hlog of a given channel, including information regarding bridged taps, attenuation, etc.

Claims

1. A method of computing the mean-square-error (MSE) noise of a line in a Digital Subscriber Line (DSL) system, the method comprising:

estimating the Hlog function of the line;

obtaining the power spectral density (PSD) function of the line;

obtaining the signal to noise ratio (SNR) function of the line;

computing the MSE noise by subtracting the SNR from the sum of the PSD and Hlog; and

storing or recording the computed MSE noise in a memory.

2. The method of claim 1 wherein obtaining the SNR function comprises computing a SNR function or collecting a reported SNR function.

3. The method of claim 2, wherein the SNR function is computed based on at least one of the following:

reported past bit distributions;

reported current bit distributions;

an initial PSD;

an Hlog function;

or

quiet line noise (QLN).

4. The method of claim 1 wherein obtaining the PSD function comprises estimating a PSD function or collecting a reported PSD function.

5. The method of claim 1 wherein estimating the Hlog function of the line comprises:

generating a plurality of attenuation data points, wherein each attenuation data point corresponds to a different frequency carrier mask and comprises an attenuation value, further wherein generating a single attenuation data point comprises;

setting a data-bearing frequency carrier mask;

transmitting data using one or more frequencies in the carrier mask;

receiving an attenuation value for the transmitted data; and

plotting the attenuation value; and

performing at least one of the following;

storing at least one of the following;

each generated attenuation data point;

or

each plotted attenuation values;

or

generating a graphical depiction of the estimated Hlog function based on the plurality of generated attenuation data points.

6. The method of claim 1 wherein estimating the Hlog function of the line comprises:

prompting the DSL system to generate operational data, wherein prompting the DSL system to generate operational data comprises;

setting an operational mode for the DSL system based on one or more of the model parameters; and

operating the DSL system using the set operational mode to generate the operational data;

collecting the generated operational data;

using the collected operational data to generate one or more model parameter values; and

recording or storing one or more of the generated model parameter values in a memory, wherein Hlog is a model parameter.

7. A Digital Subscriber Line (DSL) system estimator for computing the mean-square-error (MSE) noise of a line in a DSL system, the estimator comprising:

means for obtaining the power spectral density (PSD) function of the line;

means for obtaining the signal to noise ratio (SNR) function of the line;

estimating means configured to estimate an Hlog function of the line;

computing means coupled to the estimating means and configured to compute the MSE noise by subtracting the SNR from the sum of the PSD and the Hlog; and

means for storing or recording the computed MSE noise in a memory.

8. The DSL system estimator of claim 7, wherein the means for obtaining the SNR function further comprises a collecting means configured to collect a directly reported SNR function.

9. The DSL system estimator of claim 7, wherein the means for obtaining the SNR function further comprises:

a computing means configured to compute the SNR based on at least one of the following;

a reported past bit distribution;

a reported current bit distribution;

an initial PSD;

an Hlog function;

or

quiet line noise (QLN).

10. The DSL system estimator of claim 7, wherein the means for obtaining the PSD function further comprises an estimating means configured to estimate a PSD function or collecting means configured to collect a reported PSD function.

11. The DSL system estimator of claim 7, wherein the estimating means configured to estimate the Hlog function of the line further comprises:

a means for generating a plurality of attenuation data points, wherein each attenuation data point corresponds to a different frequency carrier mask and comprises an attenuation value, further wherein a means for generating a single attenuation data point comprises;

means for setting a data-bearing frequency carrier mask;

means for transmitting data using one or more frequencies in the carrier mask;

means for receiving an attenuation value for the transmitted data; and

means for plotting the attenuation value; and

a means for performing at least one of the following;

storing at least one of the following;

each generated attenuation data point;

or

each plotted attenuation values;

or

generating a graphical depiction of the estimated Hlog function based on the plurality of generated attenuation data points.

12. The DSL system estimator of claim 7, wherein the estimating means configured to estimate the Hlog function of the line further comprises:

means for prompting the DSL system to generate operational data, wherein prompting the DSL system to generate operational data comprises;

setting an operational mode for the DSL system based on one or more of the model parameters; and

operating the DSL system using the set operational mode to generate the operational data;

means for collecting the generated operational data;

means for using the collected operational data to generate one or more model parameter values; and

means for recording or storing one or more of the generated model parameter values in a memory, wherein Hlog is a model parameter.

13. A computer program product comprising a machine readable medium upon which is stored instructions for computing the mean-square-error (MSE) noise of a line in a Digital Subscriber Line (DSL) system, the instructions comprising:

estimating the Hlog function of the line;

obtaining the power spectral density (PSD) function of the line;

obtaining the signal to noise ratio (SNR) function of the line;

computing the MSE noise by subtracting the SNR from the sum of the PSD and Hlog; and

storing or recording the computed MSE noise in a memory.

14. The computer program product of claim 13, wherein the SNR function is directly reported.

15. The computer program product of claim 13, wherein the SNR function is computed based on at least one of the following:

reported past bit distributions;

reported current bit distributions;

an initial PSD;

an Hlog function;

or

quiet line noise (QLN).

16. The computer program product of claim 13, wherein obtaining the PSD function comprises estimating a PSD function or collecting a reported PSD function.

17. The computer program product of claim 13, wherein estimating the Hlog function of the line comprises:

generating a plurality of attenuation data points, wherein each attenuation data point corresponds to a different frequency carrier mask and comprises an attenuation value, further wherein generating a single attenuation data point comprises;

setting a data-bearing frequency carrier mask;

transmitting data using one or more frequencies in the carrier mask;

receiving an attenuation value for the transmitted data; and

plotting the attenuation value; and

performing at least one of the following;

storing at least one of the following;

each generated attenuation data point;

or

each plotted attenuation values;

or

generating a graphical depiction of the estimated Hlog function based on the plurality of generated attenuation data points.

18. The computer program product of claim 13, wherein estimating the Hlog function of the line comprises:

prompting the DSL system to generate operational data, wherein prompting the DSL system to generate operational data comprises;

setting an operational mode for the DSL system based on one or more of the model parameters; and

operating the DSL system using the set operational mode to generate the operational data;

collecting the generated operational data;

using the collected operational data to generate one or more model parameter values; and

recording or storing one or more of the generated model parameter values in a memory, wherein Hlog is a model parameter.