Abstract
In this paper, we investigate an intelligent reflecting surface
(IRS)-assisted millimeter-wave multiple-input single-output downlink wireless
communication system. By jointly calculating the active beamforming at the base
station and the passive beamforming at the IRS, we aim to minimize the transmit
power under the constraint of each user' signal-to-interference-plus-noise
ratio. To solve this problem, we propose a low-complexity machine
learning-based cross-entropy (CE) algorithm to alternately optimize the active
beamforming and the passive beamforming. Specifically, in the alternative
iteration process, the zero-forcing (ZF) method and CE algorithm are applied to
acquire the active beamforming and the passive beamforming, respectively. The
CE algorithm starts with random sampling, by the idea of distribution focusing,
namely shifting the distribution towards a desired one by minimizing CE, and a
near optimal reflection coefficients with adequately high probability can be
obtained. In addition, we extend the original one-bit phase shift at the IRS to
the common case with high-resolution phase shift to enhance the effectiveness
of the algorithms. Simulation results verify that the proposed algorithm can
obtain a near optimal solution with lower computational complexity.