Training Signal Design for Sparse Channel Estimation in Millimeter-Wave Communication with Intelligent Reflecting Surfaces

Abstract

In this paper, the problem of training signal design for millimeter-wave (mmWave) communication systems assisted by intelligent reflecting surfaces (IRSs) is considered by incorporating the sparsity in mmWave channels. The problem is approached with the Cramér-Rao bound (CRB) on the mean-square error (MSE) of unbiased channel estimation. Under the geometry-based channel model for mmWave channels, the CRB for the channel parameter of path gains and path angles is derived in closed form under the hybrid parameter assumption that the path angles are considered as deterministic parameters whereas the path gains are considered as random nuisance parameters. An algorithm to design the IRS reflection coefficients by minimizing the derived CRB is developed based on the projected gradient method (PGM). Numerical results show the effectiveness of the proposed design method for sparse channel estimation in IRS-assisted mmWave communication systems.