Cooperative Spectrum Sensing in Cognitive Radio Networks Using Hidden Markov Model

Abstract

We study the cooperative spectrum sensing in cognitive radio networks using hidden Markov model (HMM) for opportunistic spectrum access (OSA). We assume that the primary channel is operating in a TDMA manner. Thus, the spectrum sensing is operating in a slot-by-slot basis. A HMM is used for studying the cooperative spectrum sensing. In contrast to the conventional Bayesian update using only one observation in update, in this work, we propose to perform a recursive update with the observations from all the secondary users (SUs). In the proposed HMM scheme, a predefined threshold on the belief is used for determining the channel activity. With the threshold, the proposed HMM scheme is more flexible in the system operation than the simple majority vote scheme, in which no such threshold is available. We compare, by simulations, the performance of the proposed HMM scheme to that of the majority vote scheme and show that the probabilities of correctly detecting a busy state and an idle state are about 1 as the number of SUs is as large as 15, so the effects of further increase in the number of SUs are limited.