  This paper proposes a simple multi-cycle cyclostationary based signal
detection (spectrum sensing) algorithm for Orthogonal Frequency Division
Multiplexed (OFDM) signals in cognitive radio networks. We assume that the
noise samples are independent and identically distributed (i.i.d) random
variables all with unknown (imperfect) variance. Our detection algorithm employ
the following three steps. First, we formulate the test statistics as a ratio
of two quadratic cyclic autocorrelation functions. Second, we derive a closed
form expression for the false alarm probability. Third, we evaluate the
detection probability of our algorithm for a given false alarm probability. The
theoretical probability of false alarm expression matches with that of the
simulation result. Moreover, we have observed that the proposed multi-cycle
algorithm exhibits significantly superior probability of detection compared to
the existing low complexity cyclostationary based and the well known energy
detection algorithms.
