Location prediction model based on Bayesian network theory

Location prediction is one of the key technologies of active mobility management in the next generation of mobile communication systems. Most of known location prediction models only take parts of predictive factors into account, which leads to a low prediction success ratio of these models. The motivation of this paper is to design a location prediction model considering multiple predictive factors to improve the prediction success ratio and improve the efficiency of the model. In this paper, a location prediction model based on Bayesian Network theory is proposed. The proposed model can effectively solve multi-factor location prediction. Firstly, the relative predictive factors are coded in the Bayesian Network node, and location prediction results can be calculated based on cell topology information integrated in the model structure. A factors distribution mechanism is designed to solve the problem when the nodes cannot obtain location prediction information directly. Subsequently, the method of calculating location prediction results for each cell is presented. The simulation results indicate that the proposed location prediction model is effective in improving accuracy of location prediction and the stability of the model is better than that in comparative schemes.