Channel Estimation Method and Phase Shift Design for Reconfigurable Intelligent Surface Assisted MIMO Networks
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Reconfigurable intelligent surface (RIS) aided multiple-input multiple-output (MIMO) communication is becoming a serious contender for future wireless networks. The reason for this attention is due to reliable communication and low cost deployment offered by the RIS based communication systems compared to the conventional massive MIMO systems. However, the performance of an RIS assisted MIMO system heavily depends on the quality of channel state information available at both ends. As communicating devices and RIS are in close proximity, the desired channels can be modeled as line-of-sight (LOS) channels, where dominant components comes from LOS paths. Thus, leading to ill-conditioned channel matrices. To estimate these channel matrices with high quality, we propose a two stage channel estimation method for RIS aided MIMO time-division duplexing (TDD) communication systems. In particular, we employ the conventional TDD based MIMO channel estimation technique in the first stage to estimate the direct MIMO channel between terminals with no RIS. Whereas, in the second stage of the channel estimation process, we propose to use a recently developed bilinear adaptive vector approximate message passing (BAdVAMP) algorithm to estimate ill-conditioned RIS channels. The BAdVAMP method belongs to the class of approximate message passing (AMP) algorithms and it is used in wide range of estimation and learning problems. We also propose a phase shift design for the RIS using the estimated channels. Specifically, we formulate an optimization problem that maximizes the total channel gain at the user. A closed-form expression to obtain the phase shift of each passive element in the RIS is also derived. Numerical results show that the proposed BAdVAMP based RIS channel estimation performs better than its counterpart i.e., bilinear generalized AMP (BiGAMP) based RIS channel estimation scheme.
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