ML-Based Network Service Parameter Adjustment for Recurring Subscriber Faults
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Solution Overview
Problem
Existing subscriber management techniques in wireless telecommunication networks fail to provide intelligent and personalized solutions to prevent recurring service condition faults, such as payment failures, by dynamically adjusting network service parameters.
Innovation Solution
Implementing AI/ML models to intelligently select and adjust network service parameters based on subscriber-specific data to minimize the likelihood of recurring service condition faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing subscriber management techniques are used, then network service parameters remain static and simple to manage, but service condition faults recur and subscriber service availability decreases
Solution Approach 1:
The system enables self-service through AI/ML models that automatically detect service condition faults, analyze subscriber-specific data, and adjust network service parameters without manual intervention. The subscriber management system autonomously identifies patterns in historical data and implements personalized parameter adjustments to prevent fault recurrence.
Solution Approach 2:
The invention dynamically changes network service parameters based on AI/ML analysis of subscriber behavior and service conditions. The system adjusts parameters such as data limits, service priorities, and access controls according to predicted fault risks, transforming static parameters into adaptive variables that respond to real-time subscriber needs and patterns.
2Reliability
If AI/ML models are implemented to dynamically adjust network service parameters, then recurring service condition faults are reduced, but system complexity increases
Solution Approach 1:
The system performs preliminary action by using AI/ML models to predict service condition faults before they occur. The models analyze historical subscriber data and service patterns to identify at-risk scenarios, allowing the system to proactively adjust network service parameters and prevent faults before they impact service availability.
Solution Approach 2:
The invention implements feedback mechanisms where the AI/ML models continuously monitor service conditions and subscriber behavior, comparing actual outcomes with predicted patterns. This feedback loop enables the system to refine its predictions and parameter adjustments over time, improving service condition stability while managing complexity through iterative optimization.
3Reliability
If personalized network service parameter adjustments are made based on subscriber-specific data, then fault prevention effectiveness increases, but data processing requirements increase
Solution Approach 1:
The system applies local quality by tailoring network service parameter adjustments to individual subscriber characteristics and behavior patterns. Instead of uniform adjustments across all subscribers, the AI/ML models analyze subscriber-specific data to implement personalized parameter sets that target each subscriber's unique fault risk factors, maximizing prevention effectiveness while optimizing data processing resources.
Data Source
AI summary
Operation and management of a wireless telecommunication network is improved via reducing and preventing service condition faults by subscribers. Service condition faults occur when a subscriber fails to satisfy service conditions or requirements for receiving network services provided by the wireless telecommunication network. Service conditions faults can be intelligently reduced or prevented based on adjusting network service parameters or configurations for a subscriber in a manner that reduces a magnitude and/or volume of service conditions for the subscriber. A wireless telecommunication network implements a machine learning (ML) model that uses subscriber-specific data to classify a recurrence likelihood of the subscriber and a service condition fault, and based on the classification, select modified network service parameters or configurations to implement specifically for the subscriber.