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In this paper, we investigate channel estimation for a large intelligentsurface (LIS) aided multiple-input multiple-output (MIMO) communication system.Due to the close proximity of communication devices and LIS terminal, thedesired channels can be modeled as line-of-sight channels which areill-conditioned. To estimate these channel matrices with high quality, wepropose a two-stage channel estimation method. In particular, we employ theconventional time-division duplexing based MIMO channel estimation technique inthe first stage to estimate the direct MIMO channel between the end terminals.In the second stage, we propose to use a recently developed bilinear adaptivevector approximate message passing (BAdVAMP) algorithm to estimateill-conditioned LIS channels. The BAdVAMP method has been shown to be accurateand robust for ill-conditioned dictionary learning problems in compressedsensing. We also propose a phase shift design for the LIS using the estimatedchannels. Specifically, we formulate an optimization problem that maximizes thetotal channel gain at the user. A closed-form expression to obtain the phaseshift of each passive element in the LIS is also derived. Numerical resultsshow that the proposed BAdVAMP based LIS channel estimation performs betterthan its counterpart scheme bilinear generalized AMP (BiGAMP), especially whenchannel matrices are ill-conditioned.

Channel Estimation Method and Phase Shift Design for Reconfigurable Intelligent Surface Assisted MIMO Networks