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.

SpatioTemporal Position Prediction Model for Mobile Users Based on LSTM In Mobile Edge Computing (MEC), the services that a user receives change dynamically with location due to the useru0027s mobility. If we mine the useru0027s location data, predicting the useru0027s next location, we can get the useru0027s services to be used. It is convenient for the edge server to preload the useru0027s services. When users reaches predicted location, the edge servers near users provide timely services. Therefore, this paper proposes a Spatiotemporal Position Prediction Model (SPPM) for Mobile Users Based on LSTM (Long ShortTerm Memory) model in the mobile edge computing. Firstly, the time series feature extraction method is used to preprocess the historical location data of the mobile user. Next, the model uses the PCA data dimensionality reduction algorithm to process the data and then uses the LSTM model to predict the next spatiotemporal trajectory point of the mobile user. Finally, using the 17621 user trajectory data of the Geolife GPS trajectory data set, the algorithm is tested and verified. The experimental results show that the SPPM model proposed in this paper has higher prediction accuracy and more accurate prediction position.