Training Signal Design for Sparse Channel Estimation in Intelligent Reflecting Surface-Assisted Millimeter-Wave Communication

In this paper, the problem of training signal design for intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) communication under a sparse channel model is considered. The approached based on Cramér-Rao lower bound (CRB) mean-square error (MSE) estimation. By exploiting structure mmWave channels, CRB parameter composed path gains and angles derived in closed form Bayesian hybrid assumptions. Based derivation analysis, an IRS reflection pattern method proposed by minimizing as function variables constant modulus constraint coefficients. Extensions to multi-antenna transceiver, uniform planar array (UPA)-based IRS, multi-user case are discussed. Numerical results validate effectiveness