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US-20260128940-A1 - INTELLIGENT CROSS DOMAIN ANOMALY DETECTION AND PREDICTION IN TELECOMMUNICATION NETWORKS

US20260128940A1US 20260128940 A1US20260128940 A1US 20260128940A1US-20260128940-A1

Abstract

Systems and methods to perform cross domain anomaly detection and prediction in telecommunications networks. One system includes a processing system including one or more electronic processors. The processing system may be configured to: receive KPI data describing a performance of a telecommunications network, where the KPI data includes a first value of a first KPI and a second value of an associated counter of the first KPI. The processing system may be configured to: provide the KPI data to a seasonal autoregressive integrated moving average (SARIMA) model configured to detect an anomaly in the performance of the telecommunications network based on the KPI data. The processing system may be configured to: receive, from the SARIMA model, an indication that the KPI data includes the anomaly. The processing system may be configured to: responsive to receipt of the indication, provide an automated notification of the anomaly to a remote device.

Inventors

  • Sumugam Balachandran

Assignees

  • DISH WIRELESS L.L.C.

Dates

Publication Date
20260507
Application Date
20241107

Claims (20)

  1. 1 . A system to perform cross domain anomaly detection and prediction in telecommunications networks, the system comprising: a processing system comprising one or more electronic processors, the processing system configured to: receive key performance indicator (KPI) data relating to a plurality of KPIs describing a performance of a telecommunications network, wherein the KPI data includes a first value of a first KPI of the plurality of KPIs and a second value of an associated counter of the first KPI; provide the KPI data to a seasonal autoregressive integrated moving average (SARIMA) model, wherein the SARIMA model is configured to detect an anomaly in the performance of the telecommunications network based on the KPI data; receive, from the SARIMA model, an indication that the KPI data includes the anomaly; and responsive to receipt of the indication, provide an automated notification of the anomaly to a remote device.
  2. 2 . The system of claim 1 , wherein the SARIMA model is configured to: determine whether the first value of the first KPI exceeds a first threshold; and determine whether the second value of the associated counter of the first KPI exceeds a second threshold.
  3. 3 . The system of claim 2 , wherein the SARIMA model is configured to: detect the anomaly when the first value of the first KPI exceeds the first threshold.
  4. 4 . The system of claim 2 , wherein the SARIMA model is configured to: detect the anomaly when the second value of the associated counter of the first KPI exceeds the second threshold.
  5. 5 . The system of claim 2 , wherein the SARIMA model is configured to: detect the anomaly when the first value of the first KPI exceeds the first threshold and the second value of the associated counter of the first KPI exceeds the second threshold.
  6. 6 . The system of claim 1 , wherein the KPI data is time series data.
  7. 7 . The system of claim 1 , wherein the plurality of KPIs includes at least one of: an accessibility KPI, a retainability KPI, a mobility KPI, an integrity KPI, an availability KPI, or a utilization KPI.
  8. 8 . The system of claim 1 , wherein the KPI data includes a third value of a second KPI of the plurality of KPIs and a fourth value of an associated counter of the second KPI, and wherein the SARIMA model is configured to detect the anomaly when the first value of the first KPI exceeds a first threshold and the third value of the second KPI exceeds a third threshold.
  9. 9 . A method to perform cross domain anomaly detection and prediction in telecommunications networks, the method comprising: receiving, with a processing system including one or more electronic processors, key performance indicator (KPI) data relating to a plurality of KPIs describing a performance of a telecommunications network, wherein the KPI data includes a first value of a first KPI of the plurality of KPIs and a second value of an associated counter of the first KPI; providing, with the processing system, the KPI data to a seasonal autoregressive integrated moving average (SARIMA) model configured to detect an anomaly based on the KPI data; receiving, with the processing system, from the SARIMA model, an indication that the KPI data includes the anomaly; and providing, with the processing system, an automated notification of the anomaly to a remote device.
  10. 10 . The method of claim 9 , further comprising: detecting, with the processing system, via the SARIMA model, that the first value of the first KPI indicates the anomaly, wherein the automated notification indicates that the anomaly is associated with the first KPI.
  11. 11 . The method of claim 9 , further comprising: detecting, with the processing system, via the SARIMA model, that the second value of the associated counter of the first KPI indicates the anomaly, wherein the automated notification indicates that the anomaly is associated with the associated counter of the first KPI.
  12. 12 . The method of claim 9 , further comprising: determining, with the processing system, a classification of the anomaly based on at least one of: whether the first value is indicative of the anomaly or whether the second value is indicative of the anomaly; wherein the automated notification indicates the classification of the anomaly.
  13. 13 . The method of claim 9 , wherein receiving, with the processing system, the KPI data includes receiving KPI data that includes a third value of a second KPI of the plurality of KPIs and a fourth value of an associated counter of the second KPI, and wherein the SARIMA model is configured to detect the anomaly based on at least one of: (a) the first value and the second value; (b) the third value and the fourth value; or (c) the first value and the fourth value.
  14. 14 . The method of claim 9 , wherein receiving, with the processing system, the KPI data includes receiving, with the processing system, time series data.
  15. 15 . The method of claim 9 , further comprising: determining, with the processing system, via the SARIMA model, a seasonal pattern within the KPI data, wherein the seasonal pattern is associated with the first KPI and recurs within the KPI data at an interval; and wherein the SARIMA model is configured to detect the anomaly based on: the seasonal pattern; and at least one of the first value or the second value.
  16. 16 . A non-transitory computer-readable medium storing instructions that, when executed by one or more electronic processors of a processing system in a telecommunications network, cause the processing system to perform operations comprising: receiving key performance indicator (KPI) data relating to a plurality of KPIs describing a performance of the telecommunications network, wherein the KPI data includes a first value of a first KPI of the plurality of KPIs and a second value of an associated counter of the first KPI; providing the KPI data to a seasonal autoregressive integrated moving average (SARIMA) model, wherein the SARIMA model is configured to detect an anomaly based on the KPI data; receiving, from the SARIMA model, an indication that the KPI data indicates the anomaly; and responsive to receiving of the indication, providing an automated notification of the anomaly to a remote device.
  17. 17 . The computer-readable medium of claim 16 , wherein the SARIMA model is configured to detect the anomaly when the first value of the first KPI exceeds a first threshold.
  18. 18 . The computer-readable medium of claim 16 , wherein the SARIMA model is configured to detect the anomaly when the second value of the associated counter of the first KPI exceeds a second threshold.
  19. 19 . The computer-readable medium of claim 16 , wherein the SARIMA model is configured to determine a recurring fluctuation within the KPI data for the first KPI, wherein the recurring fluctuation recurs within the KPI data at a temporal interval; and wherein the SARIMA model is configured to detect the anomaly based on: the recurring fluctuation; and at least one of the first value or the second value.
  20. 20 . The computer-readable medium of claim 16 , further comprising: determining a classification of the anomaly based on whether the anomaly is based on the first value or the second value; and wherein the automated notification indicates the classification of the anomaly.

Description

BACKGROUND Wireless networks that transport digital data and telephone calls are becoming increasingly sophisticated. Currently, Fifth Generation (5G) broadband cellular networks are being deployed around the world. These 5G networks use emerging technologies to support data and voice communications with millions, if not billions, of mobile phones, computers, and other devices. 5G technologies are capable of supplying much greater bandwidths than previously available technologies. The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. SUMMARY Various aspects of the present disclosure relate to intelligent cross domain anomaly detection and prediction in telecommunication networks, and, in particular, to using a seasonal autoregressive integrated moving average (SARIMA) model for intelligent cross domain anomaly detection and prediction in open radio access network (Open RAN or ORAN) cloud native 5G standalone (SA) network. According to one aspect of the present disclosure, a system for cross domain anomaly detection and prediction in telecommunication networks. The system may include a processing system including one or more electronic processors. The processing system may be configured to receive key performance indicator (KPI) data relating to a plurality of KPIs describing a performance of a telecommunications network, where the KPI data may include a first value of a first KPI of the plurality of KPIs and a second value of an associated counter of the first KPI. The processing system may be configured to provide the KPI data to a seasonal autoregressive integrated moving average (SARIMA) model, where the SARIMA model may be configured to detect an anomaly in the performance of the telecommunications network based on the KPI data. The processing system may be configured to receive, from the SARIMA model, an indication that the KPI data includes the anomaly. The processing system may be configured to, responsive to receipt of the indication, provide an automated notification of the anomaly to a remote device. According to another aspect of the present disclosure, a method for cross domain anomaly detection and prediction in telecommunication networks. The method may include receiving, with a processing system including one or more electronic processors, key performance indicator (KPI) data relating to a plurality of KPIs describing a performance of a telecommunications network, where the KPI data includes a first value of a first KPI of the plurality of KPIs and a second value of an associated counter of the first KPI. The method may include providing, with the processing system, the KPI data to a seasonal autoregressive integrated moving average (SARIMA) model configured to detect an anomaly based on the KPI data. The method may include receiving, with the processing system, from the SARIMA model, an indication that the KPI data includes the anomaly. The method may include providing, with the processing system, an automated notification of the anomaly to a remote device. According to another aspect of the present disclosure, a non-transitory computer-readable medium is provided. The non-transitory computer-readable medium stores instructions that, when executed by one or more electronic processors of a processing system in a telecommunications network, may cause the processing system to perform operations comprising: receiving key performance indicator (KPI) data relating to a plurality of KPIs describing a performance of the telecommunications network, where the KPI data includes a first value of a first KPI of the plurality of KPIs and a second value of an associated counter of the first KPI; providing the KPI data to a seasonal autoregressive integrated moving average (SARIMA) model, where the SARIMA model is configured to detect an anomaly based on the KPI data; receiving, from the SARIMA model, an indication that the KPI data indicates the anomaly; and, responsive to receiving of the indication, providing an automated notification of the anomaly to a remote device. BRIEF DESCRIPTION OF THE DRAWINGS The following drawings are provided to help illustrate various features of examples of the disclosure and are not intended to limit the scope of the disclosure or exclude alternative implementations. FIG. 1 illustrates an example of a telecommunications network in accordance with various aspects of the present disclosure. FIG. 2 illustrates an example of a service-based architecture for a telecommunications network in accordance with various aspects of the present disclosure. FIG. 3 schematically illustrates an example of a server in accordance with various aspects of the present disclosure. FIG. 4 schematically illustrates an example of an anomaly detection server in accordance with various aspects of the present disclosure. FIG. 5 is a flowchart of an example method perform cross domain a