  Spectrum sensing is of critical importance in any cognitive radio system.
When the primary user's signal has uncertain parameters, the likelihood ratio
test, which is the theoretically optimal detector, generally has no closed-form
expression. As a result, spectrum sensing under parameter uncertainty remains
an open question, though many detectors exploiting specific features of a
primary signal have been proposed and have achieved reasonably good
performance. In this paper, a neural network is trained as a detector for
modulated signals. The result shows by training on an appropriate dataset, the
neural network gains robustness under uncertainties in system parameters
including the carrier frequency offset, carrier phase offset, and symbol time
offset. The result displays the neural network's potential in exploiting
implicit and incomplete knowledge about the signal's structure.
