Channel estimation and tracking for IRS-assisted MIMO communications using tensor modeling

Abstract

Over the last few years, intelligent reflecting surface (IRS)-assisted networks have been extensively studied as one of the possible technologies to be deployed in developing sixth generation (6G) wireless networks to achieve its vital system integration and data transmission features. In this dissertation, we use tensor algebra to model and solve channel estimation and tracking problems using parametric models in the context of IRS-assisted networks. The dissertation is divided into three parts. First, we consider an unstructured estimation of (quasi)- static channels using a Tucker modeling of the reflected pilot signals. The problem is solved using either an iterative alternating least squares (ALS) algorithm or a closed-form higher order singular value decomposition (HOSVD) algorithm. In the second part, to acquire an estimation of the concatenated channel, we formulate a parameter estimation for a hybrid scenario where the channel between the base station (BS) and the IRS is static, while the channel between the IRS and the user equipment (UE) is time-varying. In the third part, we extend our previously proposed model to a more general scenario, where the overall UE-IRS-BS channel has a two-time scale double time-varying structure. In this case, the receiver processing has two stages. The first stage estimates the spatial signatures of the involved channels based on a 4th-order constrained PARAFAC model. In contrast, the second stage performs channel tracking and data detection by exploiting a Tucker model for the received data tensor. The performance of the proposed methods is also studied numerically in terms of normalized mean square error (NMSE), computational complexity, and bit error ratio (BER).