Optimal Transmit Antenna Selection Strategy for MIMO Wiretap Channel Based on Deep Reinforcement Learning

Abstract

Antenna selection is often used for physical layer security to implement secure communications. However, due to the rapid changes of the main channel and the feedback delay of the channel state information (CSI), the transmitter obtains outdated CSI, and the outdated CSI leads to the outdated optimal transmit antenna. In order to improve the security of the system based on outdated CSI, in this paper, we propose a deep reinforcement learning framework of Deep Q Network (DQN) to predict the optimal transmit antenna in the multiple input multiple output (MIMO) wiretap channel. The legitimate receiver receives the pilot signals from each transmitting antenna, and the signal-to-noise ratio (SNR) of the pilot signals transmitted by each transmitting antenna can be obtained through maximal ratio combining. And then the legitimate receiver uses the DQN to predict the transmitting antenna at the next moment according to these SNRs. The simulation results show that DQN algorithm can effectively predict the optimal antenna at the next moment, and reduce the secrecy outage probability of MIMO wiretap system, compared with the traditional algorithm.