Learning Based Power Control for mmWave Massive MIMO against Jamming

Abstract

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have to address smart jammers that use smart radio devices to choose the jamming policy with the goal of interrupting the ongoing transmissions. In this paper, we propose a reinforcement learning based power control strategy for the downlink mmWave massive MIMO systems. More specifically, we present a fast policy hill-climbing based power control algorithm for a base station to choose the transmit power over multiple antennas. Based on the signal-to-interference-plus-noise ratio (SINR) of the signals and the jamming strength, we evaluate the impact of the number of transmit antennas on the communication performance. Simulation results verify that the proposed schemes can increase the average SINR, sum data rate and the utility of the mmWave massive MIMO against smart jamming compared with the benchmark strategy.