Machine Learning-Based Antenna Selection in Untrusted Relay Networks

This paper studies the transmit antenna selection based on machine learning (ML) schemes in untrusted relay networks. First, the exhaustive search antenna selection scheme is stated. Then, we implement three ML schemes, namely, the support vector machine-based scheme, the naïve-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, it can be concluded 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 exhaustive search scheme. However, when the training is completed, the proposed schemes can perform the antenna selection with a small computational complexity.