Abstract
Wireless cellular traffic prediction is a critical issue for researchers and practitioners in the 5G/B5G field. However, it is very challenging since the wireless cellular traffic usually show high nonlinearities and complex patterns. Most existing wireless cellular traffic prediction methods, lacking abilities of modeling the dynamic spatial-temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. To improve the accuracy of 5G/B5G cellular network traffic prediction, more cross-domain data was considered, a cross-service and regional fusion transfer learning strategy (Fusion-transfer) based on the spatial-temporal cross-domain neural network model (STC-N) was proposed. Multiple cross-domain datasets were integrated. The training accuracy of the target service domain based on the data characteristics of its source service domain according to the similarity between services and the similarity between different regions was improved, so the predictive performance of the model was enhanced. The experimental results show that the prediction accuracy of the traffic prediction model is significantly improved after the integration of multiple cross-domain datasets, the RMSE performance of SMS, Call and Internet service can be improved about 8.39%, 13.76% and 5.7% respectively. In addition, compared with the existing transfer strategy, the RMSE of the three services can be improved about 2.48%~13.19%.
Highlights
As the age of 5G/B5G comes, the number of mobile devices and the Internet of things is showing an exponential growth worldwide, and people’s demand for wireless mobile data is growing rapidly
The source domain and the target domain can share the model parameters, namely the model trained in the source domain through a large amount of data is applied to the target domain for prediction
Taking the spatio-temporal cross-domain neural network (STC-N) as the benchmark model, different types of cross-domain big datasets are taken as the research objects to discuss the influences of different numbers of cross-domain big data on the traffic prediction accuracy
Summary
As the age of 5G/B5G comes, the number of mobile devices and the Internet of things is showing an exponential growth worldwide, and people’s demand for wireless mobile data is growing rapidly. Q. Zeng et al.: Traffic Prediction of Wireless Cellular Networks Based on Deep Transfer Learning and Cross-Domain Data of transportation makes it possible for people to get from one end of the city to the other in a short time. Zeng et al.: Traffic Prediction of Wireless Cellular Networks Based on Deep Transfer Learning and Cross-Domain Data of transportation makes it possible for people to get from one end of the city to the other in a short time This makes the spatial dependency of wireless service traffic local, and a large-scale global dependency. This paper proposes a deep learning method for regional traffic prediction based on multiple cross-domain big data, and analyzes in detail the gains to the model after adding different numbers of cross-domain big datasets. To facilitate the formulation of its data below, the cleaned wireless cellular traffic data, crossdomain data and Milan city are divided into 100 ×100 grid area for one-to-one correspondence
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