  This paper considers the problem of spectrum sensing in cognitive radio
networks when the primary user employs Orthogonal Frequency Division
Multiplexing (OFDM). We develop cooperative sequential detection algorithms
based on energy detectors and the autocorrelation property of cyclic prefix
(CP) used in OFDM systems and compare their performances. We show that
sequential detection provides much better performance than the traditional
fixed sample size (snapshot) based detectors. We also study the effect of model
uncertainties such as timing and frequency offset, IQ-imbalance and uncertainty
in noise and transmit power on the performance of the detectors. We modify the
detectors to mitigate the effects of these impairments. The performance of the
proposed algorithms are studied via simulations. It is shown that energy
detector performs significantly better than the CP-based detector, except in
case of a snapshot detector with noise power uncertainty. Also, unlike for the
CP-based detector, most of the above mentioned impairments have no effect on
the energy detector.
