# A Study of Cellular Traffic Data Prediction by Kernel ELM with Parameter Optimization
###### Abstract  
Accurate and efficient prediction of mobile network traffic in a public setting with changing flow of people can not only ensure a stable network but also help operators make resource scheduling decisions before reasonably allocating resources. Therefore, this paper proposes a method based on kernel extreme learning machine (kELM) for traffic data prediction. Particle swarm optimization (PSO), multiverse optimizer (MVO), and moth-flame optimization (MFO) were adopted to optimize kELM parameters for finding the best solution. To verify the predictive performance of the kernel ELM model, backpropagation (BP) neural network, \(v\)-support vector regression (\(\tau\)SVR), and ELM were also applied to traffic prediction, and the results were compared with kELM. Experimental results showed that the smallest mean absolute percentage error in the test (11.150%) was achieved when kELM was optimized by MFO with Gaussian as the kernel function, that is, the prediction result of MFO-kELM was the best. This study can provide significant guidance for network stability and resource conservation. Keywords:traffic data prediction; public setting; kernel extreme learning machine; parameter optimization +
Footnote †: journal: Medical  
connect to the network again and again, which has a huge secondary shock on wireless networks and core networks. For example, when a concert is being held in a gym, the network traffic suddenly increases due to early failure in trying to support communication, resulting in overload of the main control card and baseband card in the base station equipment, causing card congestion failure. Due to this failure, the speed of internet access sharply reduces, and users cannot access the internet and make calls. The demand for mobile network traffic data presents a remarkable spatiotemporal characteristic. A sudden increase in traffic data demand often occurs in settings with changing flow of people, such as commercial streets, stadiums, and railway stations. The prediction of cellular traffic data in settings with changing traffic flow allows operators to understand network traffic demand in advance and achieve on-demand distribution through resource scheduling. This could effectively alleviate network pressure and reduce the impact and damage caused by a sudden increase in traffic demand on the network as well as improve user experience. The radio system has been optimized for maximum load, which results in excessive energy waste in case of low traffic [3]. The information and communication technology (ICT) industry consumed 3-4% of the world's electricity in 2008, and this use has been growing rapidly by doubling every decade [4]. Prediction of cellular traffic could help to allocate network resources on-demand, selectively shut down and rest base stations, and also reduce the power consumption of networked devices. Therefore, mobile network traffic prediction is not only of great significance in network security but also in energy conservation. There have been some relevant reports on prediction of cellular traffic data [5; 6; 7; 8; 9]. [PERSON] et al. [5] described the regularized orthogonal matching tracking (BT-ROMP) cellular flow prediction method based on threshold control. The theoretical basis of this method is compressed sensing technology; the principle is complex, and the calculation is time-consuming. In addition, the rational setting of threshold requires mass experimentation. [PERSON] and [PERSON] [6] reported a mobile communication base station traffic prediction method based on the vector autoregressive model. The autoregressive model is simple and easy to implement, but the learning ability is weak. Meanwhile, the prediction accuracy needs to be improved. [PERSON] et al. [7] investigated the backhaul resource allocation problem on one side of the base station. Flow prediction model was established using an artificial neural network. Compared with the autoregressive model, the prediction performance was improved.