Channel Estimation for Practical Intelligent Reflecting Surface-Aided Millimeter Wave MIMO-OFDM Systems

Abstract

Intelligent reflecting surface (IRS) consisting of a large number of low-cost and passive reflecting elements, has been proposed as a promising technology for future wireless communications due to its ability of customizing favorable propagation environment. In spite of its advantages, channel state information acquisition is an important and challenging task due to the passive nature of IRS. In this paper, we investigate the channel estimation problem for broadband IRS-aided millimeter wave (mm-wave) multiple-input multiple-output (MIMO) systems. From the practical implementation point of view, we consider the broadband scenario, realize the phase-amplitude-frequency relationship of the reflected signals, and adopt a practical model of reflection coefficients. Then, by utilizing the sparsity of the mm-wave channels, an efficient manifold optimization (MO) based algorithm is proposed to obtain a local optimal solution. Moreover, we propose a design approach for the optimization of the IRS reflection matrix to further improve the estimation performance. Simulation results show that the proposed MO based algorithm significantly outperforms the benchmark algorithm and the performance gain is especially significant for high-level sparse mm-wave MIMO channels.