Traffic Processing Model of Big Data Base Station Based on Hybrid Improved CNN Algorithm and K-Centroids Clustering Algorithm

Abstract

Wireless communication network (WCN) is very important for providing convenient mobile network communication services. Random Phase Multiple Access (RPMA) WCN under heterogeneous network architecture is widely used in wireless network construction worldwide due to its low power consumption and high cell density. However, this kind of WCN cannot meet the application scenario of high communication quality. Therefore, this research builds an RPMA communication quality prediction model for big data wireless base stations, which combines the convolutional neural network (CNN) algorithm and the lifting regression tree algorithm. It can be used to find the elements that have obvious influence on the communication quality. The reason for choosing the convolutional neural network algorithm is that its nonlinear feature relationship search and processing ability is excellent, and its computational complexity is relatively small. However, it is a black box algorithm and cannot obtain the importance coefficients of each feature, which is not conducive to subsequent analysis. Therefore, it is also necessary to select a relatively simple modified regression tree algorithm to participate in the calculation. The model constructed by integrating convolutional neural network and lifting regression tree algorithm is the RPMA wireless big data base station communication quality prediction model. A base station planning and deployment model based on weighted K-centroids algorithm is designed to obtain a better base station deployment scheme. In the CNN-DT model, the importance coefficients of T_B_diff, P_La and P_Lo are the largest and significantly larger than those of other features, which are 0.352, 0.289 and 0.264 respectively. The weighted K-centroids clustering algorithm designed in this study has the best overall downlink reception signal (RSSI) value distribution. For RSSI bucket ''-140∼-130'', the number of test points of WK centroids model, K-means model, GMC model, Mean Shift model and spectral clustering model accounted for 1.95%, 6.25%, 4.25%, 8.22% and 7.13% respectively. The model constructed in this study based on CNN and improved regression tree algorithm can accurately predict the communication quality of wireless big data base stations. This study's main contribution is that it can be used in conjunction with the base station planning and deployment model based on weighted K-centroids algorithm to improve the accuracy and effectiveness of location selection for the RPMA wireless communication network base station.INDEX TERMS CNN, promote the regression tree, K-centroids, big data base station, WCN.