Using hidden Markov models to evaluate performance of cooperative spectrum sensing

Cooperative sensing has been shown to improve the performance of spectrum sensing in cognitive radio (CR) networks where multiple secondary users are sending local sensing information to a CR fusion centre (FC) which makes the final determination on the occupancy of a given frequency band by licensed primary users. In this study, the authors observe the use of a hidden Markov model for evaluating the performance of cooperative sensing at the FC and propose a method that uses the history of FC sensing decisions to estimate the cooperative probabilities of detection and false alarm. The proposed method enables the FC to become aware when the performance of cooperative spectrum sensing degrades without requiring knowledge of the local sensing statistics. Numerical results obtained from simulations confirm the effectiveness of the proposed method for both soft and hard combining schemes in practical scenarios with noise and/or multipath fading.