WiMAX traffic analysis and Base stations classification in terms of LRD Communication equipment generates and stores large amounts of data. In the last years the topics of selfsimilarity and longrange dependence (LRD) in communication networks has become a very popular research domain. The analysis of real data challenged the engineers as well as the researchers so LRD has become more and more used in data analysis. The theoretical goal of this paper is to highlight the particularities of WiMAX traffic from a LRD perspective. Its practical goal is to analyze the positioning of base stations in the architecture of a WiMAX network. The results show which base stations have a good localization in the topology of the network and which have not. These base stations must be repositioned when the next session of network maintenance will take place.

Hotspot Localization and Prediction for Broadband Multimedia Services in 5G Networks Due to the generation of hotspots and the rapid change of hotspot location, 5G wireless network must have higher flexibility. How to accurately predict the future traffic load is a prominent problem. In this paper, we use Gaussian Random Field (GRF)based to fit the distribution of spatial traffic density and then locate the hotspots for broadband multimedia services. At the same time, the Long ShortTerm Memory (LSTM) model is trained to predict the next moment traffic value by using the actual traffic data collected from base stations. Finally, the model is used to predict the future traffic data of base stations and locate the hotspots. Through analysis and evaluation, numerical results show that the method can effectively locate hotspots.