An Active Search Time Tuning Model Based on the Bayesian Optimization LightGBM Algorithm
A proactive search time adjustment model based on the Bayesian optimization LightGBM algorithm was pro-posed 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.Firstly,the original feature variables were pre-processed via feature engineering,and then the data were fitted together with the historical delay data by the LightGBM algorithm.Secondly,a Bayesian optimization algorithm was introduced for parameter search during the training process,and the final weighted combination was used to adjust the predicted values in combination with the real-time monitoring delay of the terminal.Finally,the high-precision prediction of 5G terminal delay is achieved.We conducted training and validation with data from a 5G distribution network pilot in the southern network of Hebei prov-ince.The results show that the proposed method can effectively achieve delay prediction and has higher prediction ac-curacy than random forest regression and XGBoost algorithms.
万方数据
维普
