  In this paper, we apply the long short-term memory (LSTM), an advanced
recurrent neural network based machine learning (ML) technique, to the problem
of transmitter selection (TS) for secrecy in an underlay small-cell cognitive
radio network with unreliable backhaul connections. The cognitive communication
scenario under consideration has a secondary small-cell network that shares the
same spectrum of the primary network with an agreement to always maintain a
desired outage probability constraint in the primary network. Due to the
interference from the secondary transmitter common to all primary
transmissions, the secrecy rates for the different transmitters are correlated.
LSTM exploits this correlation and matches the performance of the conventional
technique when the number of transmitters is small. As the number grows, the
performance degrades in the same manner as other ML techniques such as support
vector machine, $k$-nearest neighbors, naive Bayes, and deep neural network.
However, LSTM still significantly outperforms these techniques in
misclassification ratio and secrecy outage probability. It also reduces the
feedback overhead against conventional TS.
