  In cognitive radio systems, the ability to accurately detect primary user's
signal is essential to secondary user in order to utilize idle licensed
spectrum. Conventional energy detector is a good choice for blind signal
detection, while it suffers from the well-known SNR-wall due to noise
uncertainty. In this letter, we firstly propose a deep learning based signal
detector which exploits the underlying structural information of the modulated
signals, and is shown to achieve the state of the art detection performance,
requiring no prior knowledge about channel state information or background
noise. In addition, the impacts of modulation scheme and sample length on
performance are investigated. Finally, a deep learning based cooperative
detection system is proposed, which is shown to provide substantial performance
gain over conventional cooperative sensing methods.
