Blind Spectrum Sensing by Information Theoretic Criteria for Cognitive Radios

Spectrum sensing is a fundamental and critical issue for opportunistic spectrum access in cognitive radio networks. Among the many spectrum-sensing methods, the information theoretic criteria (ITC)-based method is a promising blind method that can reliably detect the primary users while requiring little prior information. In this paper, we provide an intensive treatment on the ITC sensing method. To this end, we first introduce a new overdetermined channel model constructed by applying multiple antennas or oversampling at the secondary user to make ITC applicable. Then, a simplified ITC sensing algorithm is introduced, which needs to compute and compare only two decision values. Compared with the original ITC sensing algorithm, the simplified algorithm significantly reduces the computational complexity with no loss in performance. Applying the recent advances in random matrix theory, we then derive closed-form expressions to tightly approximate both the probability of false alarm and the probability of detection. Based on the insight derived from the analytical study, we further present a generalized ITC sensing algorithm that can provide a flexible tradeoff between the probability of detection and the probability of false alarm. Finally, comprehensive simulations are carried out to evaluate the performance of the proposed ITC sensing algorithms. Results show that they considerably outperform other blind spectrum-sensing methods in certain cases.