An Efficient Channel Estimation Technique for Hybrid IRS Assisted Multiuser Wireless Communication System Based on Tensor Modelling

We address the challenging problem of channel estimation in a narrowband multiuser communication that in-corporates intelligent reflecting surfaces (IRS). To overcome the limitations of the two-hop channel in passive IRS system, we adopt a Hybrid IRS architecture, which involves integrating active sensors into a few elements of the IRS. By exploiting the sparse characteristics of the channel, we utilize parallel factor analysis (PARAFAC) tensor models to represent the training signals. We propose an algebraic algorithm to estimate the channel and evaluate the normalized mean square error (NMSE) between the estimated and actual parameters. Additionally, we explore the uniqueness of the canonical polyadic decomposition (CPD) and analyze the essential conditions necessary for achieving accurate channel estimation. Through simulation studies, we validate the effectiveness of our proposed algorithm in estimating channels in a multiuser IRS system.