Base Station Traffic Prediction based on STL-LSTM Networks

Abstract

Realizing accurate prediction of base station traffic and effectively controlling the entire network has become a major problem that needs to be solved urgently in the rapidly developing mobile communications environment. We propose a base station traffic prediction method based on STL-LSTM model, and introduce a Seasonal and Trend decomposition using Loess (STL) method based on robust local weighted regression to achieve smoothness. By this method, the trend, period, and noise of the base station data are separately decomposed to achieve efficient use of data. Then the paper introduces a long-term short-term memory network (LSTM), uses its back-propagation time training and overcomes the characteristics of the disappearance gradient to predict the processed data to achieve the prediction closest to the true value. The experimental results show that using this algorithm to predict the base station traffic has better performance comparing with the other algorithms. And the high-accuracy prediction can be realized effectively according to the dynamic transformation of the real state of the base station traffic.