Branch-and-Bound Search and Machine Learning-Based Transmit Antenna Selection in MIMOME Channels

Abstract

The studies on the conventional transmit antenna selection (TAS) in multiple-input, multiple-output, multiple-antenna eavesdropper (MIMOME) channel have been focused on determining the optimal number of transmit antennas or selecting the optimal transmit antenna indices. To maximize the secrecy capacity, we need to develop a novel TAS scheme to simultaneously determine the optimal number of transmit antennas and select the corresponding optimal transmit antenna indices. In this paper, we propose the iterative branch-and-bound search-based TAS (IB-TAS) scheme to determine the optimal transmit antenna set in MIMOME channel. To reduce the computational complexity caused by TAS, we also propose the machine learning-based TAS (ML-TAS) schemes utilizing neural network (NN), support vector machine (SVM), and naive-Bayes (NB). Through the simulation and numerical results, it is demonstrated that the IB-TAS scheme achieves the optimal secrecy capacity. In addition, through comparative analysis of the proposed ML-TAS schemes, it was shown that the NN-based TAS scheme minimizes the computational complexity while minimizing the loss of secrecy capacity compared to other ML-TAS schemes.