Traffic Prediction of Mobile Communication Base Station Based on Elman Neural Network Model

With the fast advancement of mobile communication technology, 4G and 5G networks not only provide enormous convenience to people’s lives, but they also produce an explosion of mobile traffic. It becomes more and more important to study the traffic load of mobile communication base stations. Simultaneously, in the age of big data information, it is possible to obtain real-time feedback of base station traffic data. By acquiring information about traffic changes in mobile communication base stations, base station building models may be developed to address issues such as base station traffic load. In this study, the dynamic Elman neural network is used to anticipate traffic at communication base stations, compared with the BP neural network and convolutional neural network in MATLAB simulation experiment. The results of the experiments reveal that, the Elman neural network model is more accurate. It can provide some help for improving the accuracy of base station traffic forecast in practical applications.