5G mobile base station flow prediction analysis system based on big data

Abstract

The invention discloses a 5G mobile base station flow prediction analysis system based on big data. The 5G mobile base station flow prediction analysis system comprises a data acquisition unit, a spatial-temporal correlation calculation unit, a characteristic analysis unit, a base station cooperation relationship calculation unit, a satellite image acquisition and analysis unit, a base station pedestrian flow data acquisition unit, a pedestrian flow data prediction unit and a prediction analysis unit. A surrounding related base station having high spatial-temporal correlation with a target base station and a cooperative base station are jointly used as a data acquisition basis for base station flow prediction, the flow of the target base station can be dynamically predicted, and a topographic image of the base station and a high-rise building arrangement image are acquired by using a satellite image acquisition and analysis unit. Then the acquired topographic image and the high-rise building arrangement image are analyzed, the hindrance quantity of the topography and the high-rise building to the base station electric carrier emission is analyzed, and the hindrance quantity is fused into the prediction analysis model as a dependent variable, so that the accuracy of base station flow prediction can be further improved.

Description

technical field [0001] The present invention relates to the field of mobile communication technology, in particular to a big data-based 5G mobile base station flow prediction and analysis system. Background technique [0002] As cellular technology enters the 5G era, the number of global mobile devices and the scale of the Internet of Things are increasing exponentially. While these devices facilitate human life, they also generate massive mobile data in the 5G network, causing a huge traffic load on the 5G network. . At the same time, the increasingly abundant mobile data records also provide data support for the intelligent management of cellular networks. As the basis of intelligent management, traffic prediction is widely used in research fields such as base station green energy saving and traffic offloading. [0003] Accurate prediction of urban mobile communication base station traffic plays an important role in the congestion control of key base stations and the sel...

AI Technical Summary

Problems solved by technology

[0003] Accurate prediction of urban mobile communication base station traffic plays an important role in the congestion control of key base stations and the selection of new base station sites. Base station traffic data is not only a static representation of the region, but also reflects the flow characteristics of regional personnel. Base station traffic has nonlinear chaotic characteristics. However, traditional linear time series methods such as autoregressive moving average models are difficult to effectively capture the complex nonlinear factors in the actual base station traffic sequence. At the same time, only considering the time series of a single base station and ignoring the influence of neighboring base stations cannot reflect the dynamic characteristics of base station traffic.

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