Mobile Network Cell Impact Quantification and Remedial Prioritization
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Solution Overview
Problem
Current Service Assurance systems struggle to accurately quantify the real-time impact of underperforming, overloaded, or failed cells and sectors on customers and businesses in mobile networks, leading to inefficient prioritization of remedial actions due to lack of real-time visibility and reliance on manual, simplistic traffic comparisons.
Innovation Solution
A system that continuously monitors mobile network cells and sectors, identifies impacted areas, estimates the number of affected customers, and prioritizes remedial actions based on real-time data analysis of key performance indicators, customer impact, and potential business value, enabling precise and timely interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual traffic comparison methods are used to assess cell performance, then implementation simplicity is maintained, but measurement precision of customer impact deteriorates
Solution Approach 1:
The patent replaces manual traffic comparison methods with automated machine learning models that analyze multiple data sources (call detail records, network performance data, traffic patterns) to precisely estimate customer impact. This substitution of manual mechanical processes with automated intelligent systems resolves the contradiction by achieving high measurement precision while maintaining ease of implementation through automation.
Solution Approach 2:
The patent introduces intermediary elements including impact estimation models, data aggregation layers, and analysis systems that mediate between raw network data and final customer impact assessment. These intermediaries process and synthesize multiple data sources to provide accurate impact measurements, resolving the contradiction between simple implementation and precise measurement.
2Measurement precision
If real-time monitoring of all cells and customers is implemented, then measurement precision of impact quantification is improved, but device complexity and computational resources increase
Solution Approach 1:
The patent segments the monitoring system into modular components: data collection modules for different data sources, impact estimation models for different cell types, and prioritization systems for different impact levels. This segmentation reduces overall system complexity by breaking down the complex real-time monitoring task into manageable, independent modules that can be developed and maintained separately.
Solution Approach 2:
The patent implements partial monitoring by focusing computational resources on cells and areas with higher impact potential, rather than uniformly monitoring all cells at maximum detail. The system dynamically adjusts monitoring intensity based on detected anomalies and impact estimates, reducing overall system complexity while maintaining high measurement precision where it matters most.
3Measurement precision
If comprehensive data analysis is performed to accurately estimate impacted customers, then measurement precision is improved, but loss of time for processing increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing network performance data, traffic patterns, and customer information in structured formats before incidents occur. Impact estimation models are pre-trained on historical data to enable rapid inference during actual incidents. This preliminary preparation reduces processing time during real-time impact assessment while maintaining high precision through comprehensive pre-analyzed data.
Solution Approach 2:
The patent implements continuous data collection and model updating that maintains persistent analysis states rather than restarting analysis from scratch for each incident. The system continuously learns from incoming data and maintains ready-to-use impact estimation models, eliminating redundant processing and reducing time loss while improving precision through ongoing data accumulation.
Data Source
AI summary
A system, method, and computer program product are provided for quantifying real-time business and service impact of underperforming, overloaded, or failed cells and sectors, and for implementing remedial actions prioritization. In operation, a system monitors a plurality of cells or sectors associated with one or more mobile networks. The system identifies failed, underperforming, or overloaded cells or sectors in at least one impacted area from the plurality of cells or sectors associated with the one or more mobile networks. The system continuously identifies and updates a subset of the plurality of cells or sectors in the at least one impacted area. The system continuously identifies impacted customers in the at least one impacted area. The system continuously estimates a number of impacted customers in the at least one impacted area. The system continuously identifies customers who will likely be impacted in a defined upcoming amount of hours in the at least one impacted area. The system continuously estimates a number of customers that will be impacted in the defined upcoming amount of hours per the at least one impacted area. The system continuously estimates a potential business value of the at least one impacted area. Moreover, the system prioritizes remedial actions in the one or more mobile networks based on at least one of: the estimation of the number of impacted customers in the at least one impacted area; the estimation of the number of customers that will be impacted in the defined upcoming amount of hours per the at least one impacted area; and the estimation of the potential business value of the at least one impacted area.