  This paper studies the transmit antenna selection based on machine learning
(ML) schemes in untrusted relay networks. First, we state the conventional
antenna selection scheme. Then, we implement three ML schemes, namely, the
support vector machine-based scheme, the naive-Bayes-based scheme, and the
k-nearest neighbors-based scheme, which are applied to select the best antenna
with the highest secrecy rate. The simulation results are presented in terms of
system secrecy rate and secrecy outage probability. From the simulation, we can
conclude that the proposed ML-based antenna selection schemes can achieve the
same performance without amplification at the relay, or small performance
degradation with transmitted power constraint at the relay, comparing with
conventional schemes. However, when the training is completed, the proposed
schemes can perform the antenna selection with a small computational
complexity.
