Asymptotic Achievability of the Cramér-Rao Lower Bound of Channel Estimation for Reconfigurable Intelligent Surface Assisted Communication System

by   Yiming Liu, et al.

To achieve the joint active and passive beamforming gains in the reconfigurable intelligent surface assisted millimeter wave system, the reflected cascade channel needs to be estimated accurately. A lot of strategies have been proposed to make such estimations. However, they cannot guarantee the achievability of the Cramér-Rao lower bound (CRLB). To solve this issue, we first convert the estimation problem into a sparse signal recovery problem by utilizing the properties of discrete Fourier matrix and Kronecker product. Then, a joint typicality estimator is utilized to carry out the signal recovery task. We show that, through both mathematical proof and numerical simulations, the proposed estimator can in fact asymptotically achieve the CRLB.



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