Handoff Prediction for Femtocell Network in Indoor Environment Using Hidden Markov Model

With the explosive growth of indoor data traffic, the indoor communication performance has become a popular research area in the future wireless network. Femtocells have been deployed to improve the network capacity and coverage in indoor environment. The complex building topology and user behavior may result in frequent handover and transmission interruption. Thus, we propose a mobility prediction scheme to optimize the handoff process in indoor environment using Hidden Markov Model (HMM). In this scheme, we set up the prediction model to find the optimized handoff Femtocell Access Point (FAP). A typical case of office scenario is studied as example. Considering the user behaviors, we divide the whole prediction time into several periods according to the working schedule and study the movement characteristics in each period. With the complex building topology, we generate all possible trajectories and predict the user’s movement paths in these trajectories to improve the prediction accuracy. With the wall penetration loss influence, we revise the probability of connecting to FAP at the positions where have walls between FAP and connecting point. Eventually, we propose a mobility prediction scheme using HMM to forecast the next optimized handoff FAP. Simulation results show that the proposed scheme achieves a better performance compared with exiting schemes in terms of the handoff numbers and dwell time.