Handover prediction for wireless networks in office environments using Hidden Markov Model

User mobility can cause severe degradation of QoS, which can be counteracted by performing handovers at the right moment. However, to perform a robust handover is a challenging task. In this paper, we study the handover problem for mobile wireless networks in the office environment, where the users’ mobility is predictable. We propose a new mechanism using the Hidden Markov Model (HMM) to predict the Received Signal Strength (RSS) values. Based on the predicted values, each mobile user can perform the handover decision on his own using his preferred decision making algorithm. Extensive simulations were carried out for an office environment to evaluate the performance of the prediction handover scheme. The results showed that our HMM model can predict the RSS values accurately. The knowledge obtained from prediction is considered
in the handover decision making algorithm, which can result in a seamless handover. Moreover, with the knowledge about the future RSS, mobile users can avoid multiple handovers in a short time known as ”Ping-Pong” effect, which in turn reduces the signaling overheads on the network.