Network Parameter Baseline Generation for Base Station Configuration
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Solution Overview
Problem
Current methods for configuring network parameters in base stations are time-consuming and prone to errors, leading to inefficient operation and poor service quality due to the manual effort required by multiple engineering teams, which wastes computing and networking resources.
Innovation Solution
A management system that aggregates network data from base stations, removes irrelevant data, applies weights to network factors, and processes them to generate baselines for network parameters, automatically identifying anomalies and implementing corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of network parameters is performed by multiple engineering teams, then network parameters can be configured, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables self-service by having base stations automatically configure their own network parameters based on aggregated data from the network. The management system collects network data, determines baselines, and automatically applies corrections without requiring manual intervention from engineering teams, thus improving both accuracy and speed.
Solution Approach 2:
A management system acts as an intermediary between raw network data and base station configuration. This intermediary aggregates data from multiple base stations, processes it to determine baselines, and automatically applies corrections, eliminating the need for manual configuration while ensuring accurate and timely parameter setting.
2Reliability
If manual checking of network parameters is performed, then configuration errors can be identified, but computing and networking resources are wasted
Solution Approach 1:
The system performs self-diagnosis and self-correction by automatically comparing base station parameters against aggregated network baselines. The management system identifies anomalies and applies corrections without requiring extensive manual checking, thereby maintaining parameter correctness while minimizing computing resource consumption.
Solution Approach 2:
Instead of manually checking all network parameters extensively, the system applies partial action by focusing only on parameters that deviate from established baselines. The management system efficiently identifies and corrects only the anomalous parameters, reducing overall computing resource consumption while maintaining reliability.
3Productivity
If network parameters are not optimized, then base stations can operate, but service quality deteriorates
Solution Approach 1:
The system automatically optimizes network parameters through self-service mechanisms. The management system continuously monitors network data, compares it against baselines, and applies corrections to maintain optimal service quality without requiring complex manual configuration processes.
Solution Approach 2:
The system implements feedback by continuously monitoring network performance data and using it to adjust network parameters. The management system aggregates data from base stations, identifies deviations from baselines, and applies corrections that improve service quality, creating a closed-loop optimization process.
Data Source
AI summary
A device may receive network data from base stations associated with a network and may aggregate the network data to generate aggregated network data. The device may remove data that satisfies a threshold from the aggregated network data to generate a reduced set of the aggregated network data. The device may divide the reduced set of the aggregated network data into groups of network factors associated with determining network parameters for the base stations. The device may apply weights to the network factors in the groups of network factors to generate groups of weighted network factors and may generate baselines for the network parameters based on the groups of weighted network factors. The device may compare the network data and the baselines to identify one or more anomalies in the network parameters and may perform one or more actions based on the one or more anomalies.