A new method for traffic forecasting in urban wireless communication network

With the development of wireless devices and the increase of mobile users, the operator’s focus has shifted from the construction of the communication network to the operation and maintenance of the network. Operators are eager to know the behavior of mobile networks and the real-time experience of users, which requires the using of historical data to accurately predict future network conditions. Big data analysis and computing which is widely adopted can be used as a solution. However, there are still some challenges in data analysis and prediction for mobile network optimization, such as the timeliness and accuracy of the prediction. This paper proposes a traffic analysis and prediction system which is suitable for urban wireless communication networks by combining actual call detail record (CDR) data analysis and multivariate prediction algorithms. Firstly, a spatial-temporal modeling is used for historical traffic data extracting. After that, causality analysis is applied to communication data analysis for the first time. Based on causal analysis, multivariate long short-term memory models are used to predict future data for CDR data. Finally, the prediction algorithm is used to process real data of different scenes in the city to verify the performance of the entire system.