一种LTE网络小区问题定位及优化方法
By constructing an automatic step-by-step model based on XGBoost and combining it with actual operation and maintenance data for annotation and model training, the problems of large computational load and subjective bias in LTE network cell problem localization and optimization are solved, and efficient and accurate localization and optimization solutions are output.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HANGZHOU EASTCOM SOFTWARE TECH
- Filing Date
- 2022-12-28
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies involve large computational loads and consume significant computing resources in LTE network cell problem localization and optimization. They also suffer from subjective bias and low efficiency, especially as the amount of data increases.
An automatic stepwise problem solution optimization model is constructed using the XGBoost machine learning algorithm. The model parameters are optimized through training and cross-validation, and the model is labeled with actual operation and maintenance data to build first and second-order classification models, thereby achieving efficient and accurate problem localization and optimization solution output.
It has achieved efficient and accurate LTE network cell problem location and optimization, with an optimization scheme matching rate of over 97%, and processes more than 6,000 problem data per second, reducing human subjectivity and improving operation and maintenance efficiency.
Smart Images
Figure CN116193470B_ABST