Mobility

Prediction of State of Wireless Network Using Markov and Hidden Markov Model

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Executive Summary

Optimal resource allocation and higher quality of service is a much needed requirement in case of wireless networks. In order to improve the above factors, intelligent prediction of network behavior plays a very important role. Markov Model (MM) and Hidden Markov Model (HMM) are proven prediction techniques used in many fields. In this paper, the authors have used Markov and Hidden Markov prediction tools to predict the number of wireless devices that are connected to a specific Access Point (AP) at a specific instant of time. Prediction has been performed in two stages. In the first stage, they have found state sequence of wireless Access Points (AP) in a wireless network by observing the traffic load sequence in time.

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