Adaptive cooperative sensing with low overhead for cognitive radio networks

Although user cooperation improves sensing accuracy, a large number of secondary users (SUs) reporting decisions may cause significant overhead. In this paper, we propose a distributed scheme, called adaptive cooperative sensing (ACS), to reduce the sensing overhead while satisfying sensing accuracy requirements. In ACS, an anchor SU requires cooperative sensing only when it does not detect the presence of primary user (PU) by itself. When performing cooperative sensing, both selective reporting and sequential detection are employed. We derive the generalized-form expressions of false alarm and detection probabilities over Rayleigh fading channels with considering reporting errors for ACS. The sensing overheads are also analyzed. Then, we study overhead minimization problems and show that the sensing time allocation can be optimized to minimize the miss detection probability and sensing overhead, respectively. By simulations, the effectiveness and efficiency of ACS are validated.