Cellular Network Optimization via Predictive KPI Correlation
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Solution Overview
Problem
Current cellular network optimization methods are limited by their reliance on historical data and infrequent key performance indicator (KPI) updates, which restricts their ability to provide near real-time optimization and predict future network performance, leading to potential degradation in user experience due to inadequate resource allocation and traffic management.
Innovation Solution
A system that collects network information at short intervals, correlates it with statistical KPIs to generate predictive KPIs (pKPIs), and triggers proactive changes in network operations to prevent performance bottlenecks, such as redistributing users from congested cells to non-congested ones and adjusting base station coverage, thereby optimizing resource usage and reducing traffic load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If KPIs are generated every 15 minutes or more to accumulate sufficient data, then data accuracy is improved, but real-time network optimization capability deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing raw network data at high frequency (every few seconds) before KPI generation is needed. This pre-collected data reservoir enables both accurate historical analysis and rapid real-time optimization without the 15-minute delay of traditional systems.
Solution Approach 2:
The patent introduces an intermediary layer between raw data collection and KPI generation. This intermediary continuously buffers and pre-processes network data, allowing the system to generate accurate KPIs on-demand with minimal delay, thus resolving the conflict between data accuracy and real-time responsiveness.
2Stability of the object's composition
If network parameters are manually modified based on periodic KPI analysis, then network stability is improved, but optimization responsiveness deteriorates
Solution Approach 1:
The system transitions from static periodic optimization to dynamic continuous optimization. Network parameters are automatically adjusted in real-time based on continuously analyzed network data, enabling the system to adapt dynamically to changing conditions while maintaining stability through controlled adjustment mechanisms.
Solution Approach 2:
The patent implements a closed-loop feedback system where network performance is continuously monitored, analyzed, and used to automatically adjust network parameters. This real-time feedback mechanism enables rapid optimization responsiveness while maintaining network stability through systematic control.
3Productivity
If data is exported to a central database for analysis, then data processing capability is improved, but access delay increases
Solution Approach 1:
The patent segments the centralized database architecture into distributed data processing nodes. Each network element or regional node maintains and processes its own data locally, eliminating the bottleneck of centralized data export and access. This segmentation enables simultaneous data processing across multiple nodes without central coordination delays.
Data Source
AI summary
A computing platform is provided including a processor that executes instructions for optimizing operation of a cellular network. When executing the instructions, the computing platform operates (a) to probe for information exchanged between a mobile access network and a core network; (b) to retrieve statistical KPIs generated from a plurality of network elements belonging to the cellular network; (c) to generate a predictive Key Performance Indicator (pKPI) by correlating the probed information with the retrieved statistical KPIs, which enables the computing platform to predict a trend characterizing future performance of network elements such as cells; and (d) to trigger changes in the operation of the cellular network based on the predicted trend. The probing for information exchanged between a mobile access network and a core network is carried out at a rate higher than a rate of retrieving statistical KPIs generated from the plurality of network elements.