Hidden Markov Model Based User Mobility Analysis in LTE Network

Models of human mobility have broad application in fields such as mobile computing, network planning and resource preservation. In LTE network, the rapid increasing number of mobile broadband users requires the availability of enhanced data services and efficient mobility management. This paper focuses on service evolved Node B (eNodeB) prediction in LTE cellular network based on human mobility, and proposes a theoretical and factual method leveraging Hidden Markov Model (HMM). The usage of HMM allows us to consider trajectory characteristics of eNodeB accessed as unobservable parameters, and also the effects of individual's historical service eNodeBs. We experiment with factual data that derives from the real-world cellular network, and analyze how different parameters impact the prediction performance using control variate method. The result shows a prediction accuracy of 53% can be achieved. These findings are very significant for the location prediction problem. And the model exhibits more merit if adopted in factual communication system.