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 which 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 over-determined channel model constructed by applying multiple antennas or over sampling at the secondary user in order to make the 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 without losing any 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 probability of detection. Based on the insight derived from the analytical study, we further present a generalized ITC sensing algorithm which can provide flexible tradeoff between the probability of detection and 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.