On time demand traffic estimation based on DBN with Horse herd optimization for next generation wireless network

Abstract

A wireless network's increased demand for mobility means that 5G and later wireless networks must anticipate traffic demand in order to prevent disruptions. The network usage is high in day time in the city areas at that same time, the usage of the network is very low in rural regions. At night the network usage in city areas gradually decreases and gradually increases in the rural areas. When the demand for the network increases, the network traffic also increases. An effective time demand network traffic estimate technique based on deep learning is created to address such problems. The original data are gathered, then preprocessed using two strategies such asgaussian weighted average filter and min–max normalization. A sufficient range of raw data is converted using min–max normalization, and a gaussian weighted average filter is used to transform overall data from low to high quality. After that, use agglomerative clustering to split every base station into a number of groups. Using discrete wavelet transform, separate the traffic data into its high and low frequency constituents. Finally, a modified Deep Belief Network is employed to predict the network traffic. The horse herd optimization is utilized for training the neural network and optimizing the classifier’s weight. According to the simulation research, the proposed strategy can achieve 97 % accuracy with a 0.03 % Error. As a result, the proposed approach performs better than other existing techniques. Thus, the designed model predicted network traffic effective manner.