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.

I. Introduction

The future of wireless communication looks interesting with the new use cases and requirements which are quite challenging for the future sixth generation (6G) systems. Throughout the evolution of modern wireless systems, the propagation medium has remained inherently stochastic, adding complexity to the communication between transmitters and receivers [1]. While the fifth generation (5G) system continue to be deployed globally, researchers have already begun their exploration of beyond 5G (B5G) and 6G technologies. 6G aims to achieve ex-ceptional performance metrics, including ultra-high peak data rates (exceeding 1 Tbps), high reliability (over (>99.99%)), ultra-low latency (below 100μs), and remarkable energy ef-ficiency [2]. Two promising technologies such as massive multiple input multiple outputs (MIMO) and millimeter wave (mmWave), are being considered to address the exponential growth of users while enhancing reliability and reducing latency. However, these advancements pose challenges due to their high hardware costs and energy consumption, necessitating the development of energy-efficient solutions for the future [3].