Semi-Blind Channel Estimation For Ris-Aided Massive Mimo: A Trilinear Amp Approach

Zhen-Qing He,Hang Liu,Xiaojun Yuan,Ying-Jun Angela Zhang,Ying-Chang Liang

2021 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT)（2021）

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摘要

This paper studies semi-blind channel estimation for a reconfigurable intelligent surface (RIS) aided uplink massive multiple-input multiple-output (MIMO) system, in which the base station simultaneously estimates the channel coefficients and detects the partially unknown transmit symbols. We formulate the semi-blind channel estimation task as a trilinear inference problem. Based on the approximate message passing (AMP) principle, we develop a computationally efficient approach, called Trilinear AMP, to calculate the marginal posterior mean estimators of the trilinear inference problem. Simulation results demonstrate the effectiveness of the proposed Trilinear AMP approach.

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关键词

reconfigurable intelligent surface aided uplink massive multiple-input multiple-output system,semiblind channel estimation,RIS-aided massive MIMO,Trilinear AMP approach,marginal posterior mean estimators,computationally efficient approach,approximate message passing principle,trilinear inference problem,partially unknown transmit symbols,channel coefficients,base station

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