Wireless Network Configuration Optimization via Event-Simulation Fusion
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Solution Overview
Problem
Existing network planning methods are inefficient for upgrading deployed wireless mobile communications networks, lacking geographic position references and reliability in statistical indicators, and requiring modifications to both network apparatuses and mobile terminals.
Innovation Solution
An automated method and system that capture network events, combine them with simulation data to derive diagnostic indicators, allowing for real-time, automatic re-planning of network configurations to address criticalities by localizing events and calculating correlated indicators to improve service quality and coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network planning methods are used for upgrading deployed networks, then existing planning tools can be utilized, but the methods are inefficient and lack reliability in statistical indicators
Solution Approach 1:
The patent implements feedback by capturing actual network events from deployed networks and combining them with simulation data to generate diagnostic indicators. This feedback loop allows the system to learn from real network performance and adjust planning decisions accordingly, improving both reliability of indicators and efficiency of upgrading.
Solution Approach 2:
The patent introduces an intermediary system that combines simulation data with actual network event data to produce diagnostic indicators. This intermediary layer bridges the gap between theoretical planning models and actual network performance, enabling more reliable and efficient network upgrading decisions.
2Measurement precision
If network events are captured and combined with simulation data, then reliable diagnostic indicators can be derived, but the system complexity increases
Solution Approach 1:
The patent segments the complex task of network analysis into distinct components: capturing network events, obtaining simulation data, combining the data, and deriving diagnostic indicators. This segmentation makes the complex system more manageable and maintainable while improving measurement precision.
Solution Approach 2:
The patent creates a universal system that can handle multiple types of network events and simulation data to produce comprehensive diagnostic indicators. This multi-functional approach improves measurement precision across various network parameters while managing complexity through a unified framework.
3Productivity
If manual network planning and re-planning is performed, then flexibility in decision-making is maintained, but time consumption and labor requirements increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically capture network events, process simulation data, generate diagnostic indicators, and identify necessary re-planning actions. This automation significantly increases productivity while the system's structured approach maintains ease of operation through clear, data-driven decision-making.
Solution Approach 2:
The patent utilizes parameter changes by transforming network configuration parameters based on diagnostic indicators derived from combined data. This automated parameter adjustment process increases re-planning speed while maintaining operational ease through systematic, data-driven modifications.
4Reliability
If network simulation data is combined with captured events, then accurate identification of network criticalities is achieved, but data processing complexity increases
Solution Approach 1:
The patent merges simulation data with captured network events to produce comprehensive diagnostic indicators. This combining approach improves the reliability of criticality identification by considering both theoretical models and actual performance data, while the structured merging process manages data processing complexity.
Solution Approach 2:
The patent uses feedback from actual network events to validate and refine simulation data, creating more accurate diagnostic indicators. This feedback mechanism improves the reliability of criticality identification while the iterative processing approach manages data processing complexity efficiently.
Data Source
AI summary
A method of upgrading a wireless mobile communications network deployed in the field, including capturing network events from the wireless mobile communications network; obtaining network simulation data from an automated network simulation planning tool; combining the captured network events and the network simulation data to derive diagnostic indicators adapted to evidence criticalities in a current network configuration; and modifying the current network configuration to overcome the criticalities.