Hidden Markov Models Based Channel Status Prediction for Cognitive Radio Networks

Cognitive radio (CR) networks can be designed to manage the radio spectrum more e-ciently by utilizing of temporarily not used channels in primary users' licensed frequency bands. Here, the spectrum utilization can be improved signiflcantly by spectrum sharing between primary and secondary users (who are not being served by the primary system). In this paper, we propose to use so called Hidden Markov Models (HMM) to predict the spectrum occupancy of sharing radio bands. The results obtained using HMM are very promising and they show that HMM ofier a new paradigm for predicting channel behavior in cognitive radio.

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