  In this paper, we investigate cooperative spectrum sensing (CSS) in a
cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in
order to detect a primary user (PU) which possibly occupies multiple bands
simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS
framework based on a convolutional neural network (CNN), is proposed. In DCS,
instead of the explicit mathematical modeling of CSS which is hard to compute
and also hard to use in practice, the strategy for combining the individual
sensing results of the SUs is learned with a CNN using training sensing
samples. Accordingly, an environment-specific CSS which considers both spectral
and spatial correlation of individual sensing outcomes, is found in an adaptive
manner regardless of whether the individual sensing results are quantized or
not. Through simulation, we show that the performance of CSS can be improved by
the proposed DCS with low complexity even when the number of training samples
is moderate.
