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Intelligent reflecting surface (IRS) is a promising technology for beyond 5thGeneration of the wireless communications. In fully passive IRS-assistedsystems, channel estimation is challenging and should be carried out only atthe base station or at the terminals since the elements of the IRS areincapable of processing signals. In this letter, we formulate a tensor-basedsemi-blind receiver that solves the joint channel and symbol estimation problemin an IRS-assisted multi-user multiple-input multiple-output system. Theproposed approach relies on a generalized PARATUCK tensor model of the signalsreflected by the IRS, based on a two-stage closed-form semi-blind receiverusing Khatri-Rao and Kronecker factorizations. Simulation results demonstratethe superior performance of the proposed semi-blind receiver, in terms of thenormalized mean squared error and symbol error rate, as well as a lowercomputational complexity, compared to recently proposed parallel factoranalysis-based receivers.

Semi-Blind Joint Channel and Symbol Estimation in IRS-Assisted Multi-User MIMO Networks