Predicting clock drifting

Patent metadata

Field	Value
Publication number	US-11997627-B2
Application number	US-202217993557-A
Country	US
Kind code	B2
Filing date	Nov 23, 2022
Priority date	Dec 16, 2021
Publication date	May 28, 2024
Grant date	May 28, 2024

Abstract

Disclosed is a method comprising obtaining a plurality of previous clock skews, a reported temperature and a reported time, based on the plurality of previous clock skews, the reported temperature and the reported time, obtaining a prediction of the current clock skew, determining a current clock offset based on the predicted current clock skew, determining a clock adjustment based on the current clock offset and the reported time, and determining a corrected time based on the clock adjustment.

First claim

The invention claimed is:

1. An apparatus, comprising:
at least one processor; and
at least one memory including computer program code, wherein the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus to:
obtain a plurality of previous clock skews, a reported temperature and a reported time;
based on the plurality of previous clock skews, the reported temperature and the reported time, obtain a prediction of a current clock skew, wherein the prediction is obtained using a machine learning model and a training data for the machine learning model is extracted from a reference device when the reference device is subjected to different temperatures, and wherein values of the temperatures are drawn from a uniform distribution of values of the temperatures;
determine a current clock offset based on the predicted current clock skew;
determine a clock adjustment based on the current clock offset and the reported time; and
determine a corrected time based on the clock adjustment.

2. The apparatus according to claim 1 , wherein the machine learning model is comprised in a single output autoregression model.

3. The apparatus according to claim 1 , wherein the machine learning model comprises a supervised machine learning model.

4. The apparatus according to claim 1 , wherein the machine learning model is trained in conjunction with the obtained clock adjustment.

5. The apparatus according to claim 1 , wherein the machine learning model is retrained at regular time intervals.

6. The apparatus according to claim 1 , wherein the machine learning model is retrained when systematic anomalies are detected.

7. The apparatus according to claim 1 , wherein the apparatus is further caused to obtain the machine learning model.

8. A method, comprising:
obtaining a plurality of previous clock skews, a reported temperature and a reported time;
based on the plurality of previous clock skews, the reported temperature and the reported time, obtaining a prediction of a current clock skew, wherein the prediction is obtained using a machine learning model and a training data for the machine learning model is extracted from a reference device when the reference device is subjected to different temperatures, and wherein values of the temperatures are drawn from a uniform distribution of values of the temperatures;
determining a current clock offset based on the predicted current clock skew;
determining a clock adjustment based on the current clock offset and the reported time; and
determining a corrected time based on the clock adjustment.

9. The method according to claim 8 , wherein the machine learning model is trained in conjunction with the obtained clock adjustment.

10. The method according to claim 8 , wherein the machine learning model is retrained when systematic anomalies are detected and/or at regular time intervals.

11. A non-transitory computer readable medium comprising program instructions which, when executed on an apparatus, cause the apparatus to perform at least the following:
obtain a plurality of previous clock skews, a reported temperature and a reported time;
based on the plurality of previous clock skews, the reported temperature and the reported time, obtain a prediction of a current clock skew, wherein the prediction is obtained using a machine learning model and a training data for the machine learning model is extracted from a reference device when the reference device is subjected to different temperatures, and wherein values of the temperatures are drawn from a uniform distribution of values of the temperatures;
determine a current clock offset based on the predicted current clock skew;
determine a clock adjustment based on the current clock offset and the reported time; and
determine a corrected time based on the clock adjustment.

Assignees

• Nokia Solutions & Networks Oy

Inventors

• Barbu Oana-Elena
• Kiilerich Pratas Nuno Manuel
• Laselva Daniela

Classifications

• H04W56/0025 Primary

  synchronizing potentially movable access points · CPC title

• H04L7/0016

  correction of synchronization errors · CPC title

• H04W56/0055

  determining timing error of reception due to propagation delay · CPC title

• H04W56/001 Primary

  Synchronization between nodes · CPC title

• H04J3/0638 Primary

  Clock or time synchronisation among nodes; Internode synchronisation (synchronization for ring networks H04L12/422; data switching networks with synchronous transmission H04L12/43) · CPC title