Method and system for measurement and characterization of channel delays for broadband power line communications

Patent metadata

Field	Value
Publication number	US-10470161-B2
Application number	US-201615548870-A
Country	US
Kind code	B2
Filing date	Feb 5, 2016
Priority date	Feb 6, 2015
Publication date	Nov 5, 2019
Grant date	Nov 5, 2019

Abstract

Disclosed are various embodiments for a system and method of measuring and characterizing a PLC communication network comprising transmitting a plurality of packets from a PLC master device to a PLC terminal device via a PLC communication network, measuring a plurality of round-trip time (RTT) timestamps for the plurality of packets in the PLC master device, determining an observed probability density function based on the plurality of RTT timestamps, approximating a plurality of probability density functions, determining a rating for the probability density functions based on a comparison of the probability density functions to the observed probability density function, characterizing the PLC communication network based on the ratings of the plurality of probability density functions, and scheduling subsequent transmissions based on the characterization of the PLC Communication network.

First claim

Therefore, the following is claimed:

1. A method comprising:
transmitting a plurality of packets from a PLC master device to a PLC terminal device via a PLC communication network;
measuring a plurality of round-trip time (RTT) timestamps for ones of the plurality of packets in the PLC master device;
determining, in the at least one computing device, an observed probability density function based on the plurality of RTT timestamps;
approximating, in the at least one computing device, a plurality of probability density functions, the plurality of probability density functions based at least in part on a combination of a plurality of different parameterized probability distributions; and
determining, in the at least one computing device, a respective weighting of contributions and parameters for the individual ones of the plurality of probability density functions based at least in part on a comparison of the ones of the probability density functions to the observed probability density function.

2. The method of claim 1 , further comprising determining, in the at least one computing device, a quality of signal for the PLC communication network based at least in part on the plurality of RTT timestamps.

3. The method of claim 1 , further comprising characterizing, in the at least one computing device, the PLC communication network based on the respective weightings and parameters for the ones of the plurality of probability density functions.

4. The method of claim 3 , further comprising scheduling, in the at least one computing device, a plurality of subsequent transmissions based at least in part on the characterization of the PLC Communication network.

5. The method of claim 1 , wherein the comparison is based on at least one of: a Kolmogorov-Smirnov index or least square value.

6. The method of claim 1 , wherein the plurality of different probability distributions comprise at least one of: Lognormal, Gamma, Gaussian, Rician, or a combination of at least two of: Lognormal, Gamma, Gaussian, or Rician distributions.

7. The method of claim 1 , further comprising optimizing, in the computing device, the plurality of probability density functions based on at least one of: a Maximum-Likelihood Estimation or a trust region reflective algorithm.

8. A system comprising:
a PLC communication network;
a PLC terminal device connected to the PLC communication network, the PLC terminal device configured to receive a plurality of packets and transmit a plurality of responses acknowledging receipt of the plurality of packets;
a PLC master device connected to the PLC communication network, the PLC master device configured to:
transmit the plurality of packets;
receive the plurality of responses;
measure a round-trip time (RTT) for ones of plurality of packets;
determine an observed probability density function based on the measured RTT;
approximate a plurality of probability density functions, the plurality of probability density functions based at least in part on a combination of a plurality of different parameterized probability distributions; and
determine a respective weighting of contributions and parameters for the individual ones of the plurality of probability density functions based on a comparison of the ones of the probability density functions to the observed probability density function.

9. The system of claim 8 , wherein the PLC master device is further configured to characterize the PLC communication network based on an observed statistical property based at least in part on the measured RTT.

10. The system of claim 9 , wherein the PLC master device is further configured to adjust scheduling of a plurality of subsequent transmissions of packets based on the observed statistical property.

11. The system of claim 9 , wherein the observed statistical property is at least one of: a calculated probability density function (PDF), a comparison of a calculated PDF to ones of a plurality of approximated PDFs based on probability distributions, a mean of the plurality of RTT timestamps, variances in the plurality of RTT timestamps, or standard deviation of the plurality of RTT timestamps.

12. The system of claim 11 , wherein the plurality of approximated PDFs are based on at least one of Lognormal, Gamma, Gaussian, and Rician.

13. The system of claim 11 , wherein the plurality of approximated PDFs are based on at least one probability distribution, the at least one probability distribution being based on a combination of Lognormal, Gamma, Gaussian, and Rician distributions.

14. The system of claim 11 , wherein the comparison is based on at least one of: a Kolmogorov-Smirnov index or a least square value.

15. The system of claim 11 , wherein the PLC master device is further configured to optimize the approximated PDFs based at least in part on at least one of a Maximum-Likelihood Estimation or a trust region reflective algorithm.

16. The system of claim 8 , further comprising a plurality of PLC devices comprising the PLC master device and the PLC terminal device and the PLC communication network further comprises a plurality of network paths, each of the plurality of network paths representing a path between a unique pair of PLC devices of the plurality of PLC devices.

17. The system of claim 16 , wherein the plurality of PLC devices are configured to determine a corresponding quality of signal for each of the plurality of network paths.

18. The system of claim 8 , wherein the quality of signal is further determined based at least in part on at least one of: a calculated probability density function (PDF), a comparison of a calculated PDF to ones of a plurality of approximated PDFs based on probability distributions, a mean of the plurality of RTT timestamps, variances in the plurality of RTT timestamps, or standard deviation of the plurality of RTT timestamps.

19. A method comprising:
receiving, in at least one computing device, PLC configuration data, the PLC configuration data comprising a digital model of at least one PLC network segment; and
approximating, in the at least one computing device, a characterization for the PLC network segment based at least in part on a delay data generator, a plurality of weighted parameterized probability density functions, and a plurality of previously measured network delays across a plurality of network segments.

20. The method of claim 19 , wherein the characterization for the PLC network segment is further based at least in part on the PLC configuration data.

Assignees

• Akopian David
• Huang Grant
• Chen C L Philip
• Univ Texas

Inventors

• Akopian David
• Huang Grant
• Chen C L Philip

Classifications

• H04W72/30 Primary

  Resource management for broadcast services · CPC title

• H04W84/04

  Large scale networks; Deep hierarchical networks · CPC title

• H04L5/1415

  using control lines · CPC title

• G06F11/34

  Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation {; Recording or statistical evaluation of user activity, e.g. usability assessment} · CPC title

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title