Seamless and fast handover is one of main goals in Long Term Evolution (LTE) in supporting mobility and maintaining user's quality of services. Mobility prediction is a technique to identify future targeted base station in advance, to reduce handover latency, and finally to enhance handover performance in wireless networks. In this paper, mobility prediction via Markov Chains with an input of user's mobility history is proposed as a technique to predict the user's movement in femtocells deployment. The results show that our proposed method predicts better when random data is 50% and above compared to the existing method. We had also analysed the effect of unavailable base station to the accuracy of the prediction in our proposed method. From the analysis, it is found that, the length of time collecting the data for the database effect the prediction accuracy in certain duration.
The Long Term Evolution (LTE) femtocell has promised to improve indoor coverage and enhance data rate capacity. Due to the special characteristic of the femtocell, it introduces several challenges in terms of mobility and interference management. This chapter focuses on mobility prediction in a wireless network in order to enhance handover performance. The mobility prediction technique via Markov Chains and a user’s mobility history is proposed as a technique to predict user movement in the deployment of the LTE femtocell. Simulations have been developed to evaluate the relationship between prediction accuracy and the amount of non-random data, as well as the relationship between the prediction accuracy and the duration of the simulation. The result shows that the prediction is more accurate if the user moves in regular mode, which is directly proportional to the amount of non-random data. Moreover, the prediction accuracy is maintained at 0.7 when the number of weeks is larger than 50.
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