  We apply the latest advances in machine learning with deep neural networks to
the tasks of radio modulation recognition, channel coding recognition, and
spectrum monitoring. This paper first proposes an identification algorithm for
space-time block coding of a signal. The feature between spatial multiplexing
and Alamouti signals is extracted by adapting convolutional neural networks
after preprocessing the received sequence. Unlike other algorithms, this method
requires no prior information of channel coefficients and noise power, and
consequently is well-suited for noncooperative contexts. Results show that the
proposed algorithm performs well even at a low signal-to-noise ratio
