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EP-4740390-A1 - METHOD AND SYSTEM FOR IDENTIFICATION OF ONE OR MORE SERVICE DEGRADATION EVENTS

EP4740390A1EP 4740390 A1EP4740390 A1EP 4740390A1EP-4740390-A1

Abstract

The present disclosure relates to a method and a system for identification of one or more service degradation events The method encompasses: receiving, by a transceiver unit [102], a set of data from a plurality of sources; analysing, by an analysis unit [104], the set of data using a trained model; identifying, by an identification unit [106], one or more service degradation events based on the analysis of the set of data; and alerting, by an alert unit [108], a Network Management System (NMS) based on the identification of the one or more service degradation events.

Inventors

  • BISHT, SANDEEP
  • BHATNAGAR, AAYUSH
  • SINHA, ANURAG
  • Ansari, Ezaj
  • YADAV, RAVINDRA
  • PANDEY, PRASHANT

Assignees

  • Jio Platforms Limited

Dates

Publication Date
20260513
Application Date
20240611

Claims (17)

  1. 1. A method [200] for identification of one or more service degradation events, the method [200] comprising: receiving, by a transceiver unit [102] of a network node [100], a set of data from a plurality of sources; analysing, by an analysis unit [104] of the network node [100], the set of data using a trained model; identifying, by an identification unit [106] of the network node [100], the one or more service degradation events based on the analysis of the set of data; and alerting, by an alert unit [108] of the network node [100], a Network Management System (NMS) based on the identification of the one or more service degradation events.
  2. 2. The method [200] as claimed in claim 1, wherein the one or more service degradation events correspond to at least one from among an overloading situation at a 5 th generation (5G) network, an increase of traffic at the 5G network, and an occurrence of one or more service impacting errors at the 5G network.
  3. 3. The method [200] as claimed in claim 2, wherein the network node [100] is a Service Communication Proxy (SCP) of the 5G network.
  4. 4. The method [200] as claimed in claim 3, further comprising: determining, by a determination unit [110] associated with the Service Communication Proxy (SCP) of the 5G network, a system and performance statistics at a time interval that is at least one of regular, on-demand, and configurable; and fetching, by a fetching unit [112] via the trained model, the determined system and performance statistics from the SCP, wherein the time interval for fetching is set based on a policy defined by an operator, thereby enabling the trained model to monitor and analyse health and performance of a set of 5G network services in a dynamic and adaptable manner.
  5. 5. The method [200] as claimed in claim 1, wherein the plurality of sources comprises at least one of connected network nodes, client-based network functions, local servers and cloud-based servers.
  6. 6. The method [200] as claimed in claim 1, wherein the set of data comprises at least one of a traffic-based data, a signal data, a user data, a data associated with pattern of traffic at the network node, a historical data, an occurrence of events that impact performance of network, and a data associated with resolution of the one or more service degradation events.
  7. 7. The method [200] as claimed in claim 1, wherein the one or more service degradation events are identified when severity of one or more events passes a threshold value, wherein the threshold value is defined based on the analysis of the set of data using the trained model.
  8. 8. The method [200] as claimed in claim 1, wherein the trained model is a machine learning (ML) based model.
  9. 9. A network node [100] for identification of one or more service degradation events, the network node [100] comprising: a transceiver unit [102] configured to receive a set of data from a plurality of sources; an analysis unit [104] connected at least with the transceiver unit [102], the analysis unit [104] is configured to analyse the set of data using a trained model; an identification unit [106] connected at least with the analysis unit [104], the identification unit [106] is configured to identify the one or more service degradation events based on the analysis of the set of data; and an alert unit [108] connected at least with the identification unit [106], the alert unit is configured to alert a Network Management System (NMS) based on the identification of the one or more service degradation events.
  10. 10. The network node [100] as claimed in claim 9, wherein the one or more service degradation events correspond to at least one from among an overloading situation at a 5 th generation (5G) network, an increase of traffic at the 5G network, and an occurrence of one or more service impacting errors at the 5G network.
  11. 11. The network node [100] as claimed in claim 10, wherein the network node [100] is a Service Communication Proxy (SCP) of the 5G network.
  12. 12. The network node [100] as claimed in claim 11, further comprising: a determination unit [110] configured to determine a system and performance statistics at a time interval that is at least one of regular, on-demand, and configurable; and a fetching unit [112] configured to fetch via the trained model, the determined system and performance statistics from the SCP, wherein the time interval for fetching is set based on a policy defined by an operator, thereby enabling the trained model to monitor and analyse health and performance of a set of 5G network services in a dynamic and adaptable manner.
  13. 13. The network node [100] as claimed in claim 9, wherein the plurality of sources comprises at least one of connected network nodes, client-based network functions, local servers, and cloud-based servers.
  14. 14. The network node [100] as claimed in claim 9, wherein the set of data comprises at least one of a traffic-based data, a signal data, a user data, a data associated with pattern of traffic at the network node, a historical data, an occurrence of events that impact performance of network, and a data associated with resolution of the one or more service degradation events.
  15. 15. The network node [100] as claimed in claim 9, wherein the one or more service degradation events are identified when severity of one or more events passes a threshold value, wherein the threshold value is defined based on the analysis of the set of data using the trained model.
  16. 16. The network node [100] as claimed in claim 9, wherein the trained model is a machine learning (ML) based model.
  17. 17. A non-transitory computer readable storage medium storing instructions for identification of one or more service degradation events, the instructions including executable code, the executable code when executed, may cause: a transceiver unit [102] to receive, a set of data from a plurality of sources, an analysis unit [104] to analyse the set of data using a trained model, an identification unit [106] to identify, the one or more service degradation events based on the analysis of the set of data, and an alert unit [108] to alert, a Network Management System (NMS) based on the identification of the one or more service degradation events.

Description

METHOD AND SYSTEM FOR IDENTIFICATION OF ONE OR MORE SERVICE DEGRADATION EVENTS FIELD OF THE DISCLOSURE [0001] The present disclosure relates generally to the field of wireless communication systems. More particularly, the present disclosure relates to methods and systems for identification of one or more service degradation events and one or more anomalies in a wireless communication network using one or more Artificial Intelligence (Al) techniques. BACKGROUND [0002] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art. [0003] Wireless communication technology has rapidly evolved over the past few decades, with each generation bringing significant improvements and advancements. The first generation of wireless communication technology was based on analog technology and offered only voice services. However, with the advent of the second-generation (2G) technology, digital communication and data services became possible, and text messaging was introduced. The third-generation (3G) technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The fourth generation (4G) technology revolutionized wireless communication with faster data speeds, better network coverage, and improved security. Currently, the fifth generation (5G) technology is being deployed, promising even faster data speeds, low latency, and the ability to connect multiple devices simultaneously. With each generation, wireless communication technology has become more advanced, sophisticated, and capable of delivering more services to its users. [0004] With the rapid growth of 5G services and the exponential increase in the number of users accessing them, the complexity of network management has escalated significantly. This surge in user activity has led to a corresponding rise in anomalies at various critical nodes within the network, such as at the Service Communication Proxy (SCP) level. These anomalies pose a serious threat to the seamless operation of the radio communication network and must be diligently monitored to prevent any adverse impact on user experience. These anomalies have the potential to degrade the quality of services offered to users across the network. Furthermore, the anomalies in network traffic patterns could serve as indicators for potential security breaches or cyberattacks, making it imperative to address them swiftly and effectively. For instance, anomalies may manifest as sudden spikes or drops in traffic at specific nodes, unusual errors cropping up within the network infrastructure, or deviations from established patterns of operation. [0005] Further, over the period of time various solutions have been developed to improve the performance of communication devices or devices / modules configured at a network end and to prevent affecting the experience of the user in the communication network due the occurrence of such abnormalities in the network. However, still there are certain challenges with existing solutions. The conventional approach for network monitoring and management fails to operate proactively and also fails to cope with a volume and diversity of data traffic traversing the network. As a result, anomalies go undetected or inadequately addressed, leading to service disruptions and degradation of user experience. [0006] Thus, in order to improve the radio access network capacity and performance of network node(s), there exists an imperative need in the art to reliably monitor the occurrence of various abnormalities or anomalies and service degradation events at the network level and then alert various network managing entities about such events, which the present disclosure aims to address. OBJECTS OF THE DISCLOSURE [0007] Some of the objects of the present disclosure, which at least one implementation disclosed herein satisfies are listed herein below. [0008] It is an object of the present disclosure to provide a system and a method to identify service degradation event(s) at the network in real-time, periodically or near real-time. [0009] It is another object of the present disclosure to provide a solution that monitor the occurrence of service degradation events at the network and more particularly at the SCP level of the 5G core network. [00010] It is another object of the present disclosure to provide a method which proactively monitors one or more 5G core Network Function services for detecting one or more anomalies effectively. [00011] It is another object of the present disclosure to provide easy troubleshooting by automatically tracking one or more affected nodes in