Wireless cellular network flow prediction method based on deep transfer learning and cross-domain data fusion

Abstract

The invention discloses a wireless cellular network flow prediction method based on deep transfer learning and cross-domain data fusion, and belongs to the technical field of intelligent communication. Similarity among three services of short messages, telephones and the Internet and the similarity among different regions are analyzed, a plurality of cross-domain data sets is fused, and a space-time cross-domain neural network model is adopted to predict the wireless cellular traffic; a cross-service and region fusion transfer learning strategy based on a space-time cross-domain neural networkmodel (STC-N) is provided, and the prediction precision of a target domain is improved according to data characteristics of a source domain. The method can verify that the more comprehensive the considered data set is, the higher the prediction precision of the model is; in addition, the proposed transfer learning strategy can reduce the training data, calculation capability and generalization capability required for constructing the deep learning model.

Description

technical field

[0001] The invention belongs to the technical field of intelligent communication, and in particular relates to a wireless cellular network traffic prediction method based on deep migration learning and cross-domain data fusion. Background technique

[0002] With the advent of the 5G / B5G era, the number of mobile devices and the Internet of Things is growing exponentially around the world, and people's demand for wireless mobile data is growing rapidly. How to scientifically and rationally allocate and optimize existing cellular network resources, improve resource utilization, and reduce energy consumption of cellular base stations is a problem that the communication industry needs to think about and solve.

[0003] At present, the main methods of wireless cellular traffic forecasting are: (1) autoregressive integrated moving average model (ARIMA); (2) exponential smoothing method (ES); (3) linear regression method (LR); (4) support vector machine Regression ...

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