Cascaded Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser MISO Systems

Abstract

This paper investigates the uplink cascaded channel estimation for intelligent-reflecting-surface (IRS)-assisted multiuser multiple-input-single-output systems. We focus on a sub-6 GHz scenario where the channel propagation is not sparse and the number of IRS elements can be larger than the number of BS antennas. A novel channel estimation protocol without the need of on-off amplitude control to avoid the reflection power loss is proposed. In addition, the pilot overhead is substantially reduced by exploiting the common-link structure to decompose the cascaded channel coefficients by the multiplication of the common-link variables and the user-specific variables. However, these two types of variables are highly coupled, which makes them difficult to estimate. To address this issue, we formulate an optimization-based joint channel estimation problem, which only utilizes the covariance of the cascaded channel. Then, we design a low-complexity alternating optimization algorithm with efficient initialization for the non-convex optimization problem, which achieves a local optimum solution. To further enhance the estimation accuracy, we propose a new formulation to optimize the training phase shifting configuration for the proposed protocol, and then solve it using the successive convex approximation algorithm. Comprehensive simulations verify that the proposed algorithm has supreme performance compared to various stateof-the-art baseline schemes.

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

“…One of the major challenges with RIS-empowered smart wireless environments, including multiple BSs and multiple UEs, is CE, which most commonly constitutes a prerequisite for the optimized orchestration of the phase responses of the unit elements (i.e., meta-atoms) of the multiple RISs. CE involves the estimation of multiple channels simultaneously: the direct channels between each BS and each UE, the channels between each RIS and BS, and the channels between each RIS and each UE [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47]. This task becomes more complex when the deployed RISs are equipped with large numbers of unit elements having nonlinear hardware characteristics [38].…”