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Intelligent reflecting surface (IRS) is a promising technology for the 6$\text{th}$ generation of wireless systems, realizing the smart radio environment concept. This paper presents a novel tensor-based receiver for Intelligent reflecting surface (IRS)-assisted multiple-input multiple-output communications capable of jointly estimating the channels and the transmitted data streams in a semi-blind fashion. Assuming a fully passive IRS architecture and introducing a simple space-time coding scheme at the transmitter, the received signal model can be advantageously built using the PARATUCK tensor model, which can be seen as a hybrid of parallel factor analysis and Tucker models. A semi-blind receiver is derived by exploiting the algebraic structure of the PARATUCK tensor model. We first formulate a semi-blind receiver based on a trilinear alternating least squares method that iteratively estimates the two involved - IRS-base station and user terminal-IRS - communication channels and the transmitted symbol matrix. We discuss identifiability conditions that ensure the joint semi-blind recovery of the involved channel and symbol matrices and propose a joint design of the coding and IRS reflection matrices to optimize the receiver performance. We also formulate an enhanced two-stage semi-blind receiver that efficiently exploits the direct link to refine the channel and symbol estimates iteratively. In particular, we discuss the impact of an imperfect IRS absorption (residual reflection) on the performance of the proposed receiver. Numerical results are proposed for performance evaluation in several system settings in terms of the normalized mean squared error of the estimated channels and the achieved symbol error rate, corroborating the merits of the proposed semi-blind receiver in comparison to competing methods., QC 20230526

Intelligent reflecting surface (IRS) is a promising technology for beyond of the wireless communications. In fully passive IRS-assisted systems, channel estimation is challenging and should be carried out only at the base station or at the terminals since the elements of the IRS are incapable of processing signals. In this letter, we formulate a tensor-based semi-blind receiver that solves the joint channel and symbol estimation problem in an IRS-assisted multi-user multiple-input multiple-output system. The proposed approach relies on a generalized PARATUCK tensor model of the signals reflected by the IRS, based on a two-stage closed-form semi-blind receiver using Khatri-Rao and Kronecker factorizations. Simulation results demonstrate the superior performance of the proposed semi-blind receiver, in terms of the normalized mean squared error and symbol error rate, as well as a lower computational complexity, compared to recently proposed parallel factor analysis-based receivers., QC 20220830

Reconfigurable intelligent surface (RIS) is a candidate technology for future wireless networks. It is known that the promised gains of RIS-Assisted communications depend on the channel estimation performance. When the RIS is affected by imperfections, the associated phase shift responses present a non-ideal behavior, which translates into unknown, and possibly time-varying, phase deviations. Such perturbations can be caused by physical, electronic, or environmentalrelated conditions. In this scenario, traditional channel estimation schemes may fail to provide sufficiently accurate channel estimates. In this work, considering a time-varying RIS imperfection model, we propose an efficient and low-complexity tensor-based method to estimate the involved communication channels under unknown phase-shift responses. The proposed algorithm relies on a tensor modeling of the received signals and has a closed-form solution based on the higher order singular value decomposition. Simulation results show the effectiveness of our proposed solution in terms of estimation accuracy and computational complexity compared to the benchmark method., Part of ISBN 9781665474566QC 20230802