Cellular Network Pattern Detection for Self-Healing Core Configuration
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Solution Overview
Problem
Existing cellular telecommunication networks lack effective mechanisms for real-time self-improvement and pattern detection, leading to inefficiencies and potential failures due to human error and limited visibility into network behavior.
Innovation Solution
Implementing a machine learning model trained on logs from a monitoring tool within a managed container-orchestration system to predict network deficiencies and autonomously modify the network configuration using a file chart, enabling self-perfecting capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration and monitoring of cellular telecommunication networks is performed, then human control and flexibility are maintained, but human error and inefficiency increase network deficiencies
Solution Approach 1:
The system enables self-service through machine learning models that automatically detect patterns, predict deficiencies, and generate configuration modifications without human intervention. The closed-loop system autonomously monitors network state, identifies issues, and implements corrections, allowing the network to self-diagnose and self-heal configuration problems.
Solution Approach 2:
The system implements continuous feedback by monitoring network configuration state and performance metrics, comparing actual state against desired state, and automatically generating corrective actions. This closed-loop feedback mechanism enables real-time detection and correction of configuration drift, ensuring network reliability through automated adjustment based on observed conditions.
2Reliability
If real-time pattern detection and prediction are implemented, then network deficiencies are predicted and prevented, but system complexity increases
Solution Approach 1:
The system introduces an intermediary machine learning model layer between network monitoring and configuration management. This intermediary translates raw network state data into predictive insights and actionable recommendations, bridging the gap between observation and action while managing complexity through specialized AI components rather than distributed complex logic across the entire system.
Solution Approach 2:
The system segments the network management function into distinct modular components: pattern detection module, prediction module, recommendation generation module, and configuration modification module. This segmentation allows each component to specialize in a specific task, reducing overall system complexity while enabling comprehensive real-time analysis and automated response.
3Productivity
If automated modification of network configuration is performed, then human intervention is reduced and errors minimized, but risk of incorrect modifications increases
Solution Approach 1:
The system performs preliminary action by generating and evaluating multiple candidate configuration modifications before implementation. The machine learning model predicts the outcomes of potential modifications and selects the optimal one, ensuring that changes are pre-validated for correctness and safety before being applied to the live network, thereby reducing the risk of incorrect modifications.
Solution Approach 2:
The system applies preliminary anti-action by predicting potential harmful effects of configuration changes before they occur. The machine learning model evaluates candidate modifications to ensure they will not cause network deficiencies or service level agreement violations, preventing harmful actions before they can impact network performance.
Data Source
AI summary
A disclosed method may include (i) predicting a network deficiency by applying a machine learning model trained on a log from a monitoring tool that monitors a resource within a cloud computing platform on which is executing at least part of a cellular telecommunication network core that is configured within a managed container-orchestration system as specified by a file chart generated by a cloud native computing package manager and (ii) modifying how the cellular telecommunication network core is configured within the managed container-orchestration system such that the predicted network deficiency is at least partially prevented by modifying the file chart according to a recommendation of the machine learning model and deploying the modified file chart.