5G Top Offender Analysis for Real-Time Network Performance Bottlenecks
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Solution Overview
Problem
The rapid scalability and dynamic nature of 5G wireless networks make it difficult to maintain accurate and up-to-date summaries of network status, leading to potential performance issues and inefficiencies in network management.
Innovation Solution
A performance analysis system that collects, processes, and analyzes network data using machine learning models to generate customizable reports and recommendations for network optimization, including dashboards, alerts, and notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual components are rapidly deployed and scaled in 5G networks, then network capacity and scalability are improved, but maintaining accurate and up-to-date network status summaries becomes difficult
Solution Approach 1:
The patent implements automated monitoring systems that continuously collect network status data from virtual components and provide feedback loops to update network summaries in real-time. This ensures that despite rapid deployment and scaling of virtual components, the network status information remains accurate and current through continuous verification and updates.
Solution Approach 2:
The system performs preliminary validation and verification of network status data before it is incorporated into summaries. By pre-processing and validating data from rapidly deployed virtual components, the system ensures accuracy is maintained from the outset, preventing information loss before it occurs.
2Adaptability or versatility
If multiple organizations maintain different network components with their own performance goals, then organizational autonomy is preserved, but one-size-fits-all performance reports become ineffective
Solution Approach 1:
The patent enables customization of performance reports at different organizational levels and for different network components. Each organization can tailor performance metrics, thresholds, and report formats to their specific goals and requirements, while the system maintains a unified architecture that coordinates across all organizations.
Solution Approach 2:
The system provides a universal performance monitoring platform that can serve multiple organizations with different needs. It generates both organization-specific customized reports and consolidated network-wide reports, making the system adaptable to various reporting requirements while maintaining operational efficiency.
3Adaptability or versatility
If cloud-based virtualized architecture is used, then network management flexibility and scalability are improved, but the complexity of monitoring and troubleshooting increases
Solution Approach 1:
The patent consolidates multiple monitoring and troubleshooting functions into a unified automated system. By merging data collection, analysis, validation, and reporting functions into an integrated platform, the system reduces the overall complexity that would otherwise exist across multiple separate tools and processes.
Solution Approach 2:
The system implements automated self-diagnosis and self-monitoring capabilities that reduce the need for manual intervention. The automated monitoring system independently tracks, validates, and reports on network status, reducing the complexity burden on human operators while maintaining high flexibility in network management.
Data Source
AI summary
Systems and automated processes are described to provide collection of wireless network status data, such as a 5G network, and to automatically respond to queries regarding the performance of the network. Systems and automated processes may, in response to a request for network performance information, obtain performance and static data from network data sources, analyze the static data to generate validated site data, apply a KPI formula to the performance data to generate KPI data, and generate a network performance report based on the validated site data and KPI data. In addition, the systems and automated processes may use an appropriate machine learning model from a library of models to determine recommended actions to increase network performance and may automatically implement such actions. A top offender system may be implemented to analyze the status data to determine the network elements having the largest negative impact on network performance.