Base Station Fault Ranking Through Clustered Data Rate Forecasting
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Solution Overview
Problem
Existing fault management systems in telecommunication networks fail to prioritize faults based on their impact on user performance, leading to inefficient resource allocation and potential service disruptions.
Innovation Solution
A method and system using machine learning models, including time series forecasting and clustering, to predict the impact of faults on user equipment data rates, enabling service-centric fault management by identifying and prioritizing faults based on their effect on user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If equipment-centric fault detection is used, then fault identification capability is improved, but fault impact analysis and prioritization deteriorates
Solution Approach 1:
The patent segments the fault management process into two distinct phases: (1) equipment-centric fault detection using machine learning models to identify faulty base stations, and (2) service-centric impact analysis using clustering algorithms to group affected users and prioritize faults based on user experience degradation. This segmentation allows each phase to focus on its specific objective without compromising the other.
Solution Approach 2:
The patent transitions from a single-dimension equipment-centric approach to a multi-dimensional approach by introducing a service impact dimension. The system not only detects faults at the base station level but also analyzes and prioritizes them based on their impact on user services, adding a new dimension of service quality assessment to the traditional equipment monitoring dimension.
2Ease of operation
If equal weightage is given to all faults, then fault management simplicity is improved, but network operation efficiency deteriorates
Solution Approach 1:
The patent applies local quality by differentiating the treatment of faults based on their local impact characteristics. Instead of uniform treatment, the system clusters faults according to their impact on specific user groups and network segments, allowing operators to prioritize and respond differently to faults based on their localized service impact rather than treating all faults equally.
3Measurement precision
If manual fault diagnosis is used, then diagnostic accuracy is improved, but scalability and complexity management deteriorates
Solution Approach 1:
The patent implements self-service by enabling the network to autonomously detect, analyze, and prioritize faults using machine learning models and clustering algorithms. The system automatically processes network data, identifies patterns, and ranks faults based on their impact without requiring manual intervention, thereby maintaining high diagnostic accuracy while improving scalability and reducing operational complexity.
Data Source
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AI summary
This disclosure relates generally to a method and system for network fault management. Conventionally, faults are analyzed by setting rules based on network experts' experience, such as duration of faults or predefined categories of faults, to determine which faults need to be handled with higher priority. The present disclosure addresses these problems through a method of performing network fault management at a faulty base station using a timeseries forecasting model coupled with a clustering algorithm. The time series forecasting model considers a plurality of network parameters received from a plurality of base stations serving at least one user equipment (UE) and trains the model to predict an average data rate at the faulty BS. Further the model receives the network parameters for a cluster comprising the faulty BS and re-trains itself. Finally, the model prioritizes the faults based on decreased average data rate of the UE at the faulty BS.