An Active SearchTime Tuning Model Based on the Optimally Weighted LightGBM Algorithm

A proactive search time adjustment model based on the optimally weighted LightGBM algorithm was studied in this paper for the problem of large delay fluctuations in 5G distribution terminals leading to protection blocking. The model used historical data on delay fluctuations of wireless communication terminals and feature variables such as temperature and date and time as inputs to predict delays and dynamically adjust equipment parameters. First, the original feature variables were pre-processed via feature engineering, then the data were fitted together with the historical delay data by the LightGBM algorithm. And the final weighted combination was used to adjust the predicted values in combination with the real-time monitoring delay of the terminal, finally realizing the high-precision prediction of 5G terminal delay. The results show that the proposed method can effectively achieve delay prediction and has higher prediction accuracy than random forest regression and XGBoost algorithms.