Low-Complexity Channel Estimation for RIS-Assisted Multi-User Wireless Communications

TL;DR

The paper tackles the high pilot overhead challenge in RIS-assisted multi-user channels by focusing on cascaded UE-RIS-BS channel estimation under passive RIS operation. It introduces a correlated-grouping LMMSE estimator that partitions RIS elements into $N_{\mathcal{G}}$ groups and uses a two-stage estimation procedure to exploit spatial correlation, reducing the required RIS training patterns to $N_{\mathcal{G}}+1$. The authors derive the normalized MSE $\epsilon_k$ and its high-SNR limit $\epsilon_k^{(\rho_k\to\infty)}$ and demonstrate, through simulations, that the proposed method outperforms state-of-the-art grouping-based estimators under realistic exponential spatial correlation. The approach offers a practical path to reduce pilot overhead while maintaining estimation accuracy, enabling more reliable RIS deployments in environments with short coherence times.

Abstract

Reconfigurable intelligent surfaces (RISs) are eminently suitable for improving the reliability of wireless communications by jointly designing the active beamforming at the base station (BS) and the passive beamforming at the RIS. Therefore, the accuracy of channel estimation is crucial for RIS-aided systems. The challenge is that only the cascaded two-hop channel spanning from the user equipments (UEs) to the RIS and spanning from the RIS to the BS can be estimated, due to the lack of active radio frequency (RF) chains at RIS elements, which leads to high pilot overhead. In this paper, we propose a low-overhead linear minimum mean square error (LMMSE) channel estimation method by exploiting the spatial correlation of channel links, which strikes a trade-off between the pilot overhead and the channel estimation accuracy. Moreover, we calculate the theoretical normalized mean square error (MSE) for our channel estimation method. Finally, we verify numerically that the proposed LMMSE estimator has lower MSE than the state-of-the-art (SoA) grouping based estimators.

Figures (3)

• Figure 1: System model of the RIS-aided multi-user wireless communication system.
• Figure 2: Theoretical analysis and simulation results of the received SNR $\gamma$ versus the normalized MSE in different estimators, where the solid lines represent the theoretical results.
• Figure 3: Theoretical analysis and simulation results of the received SNR $\gamma$ versus the normalized MSE with different number of groups $\mathcal{G}$, where the solid lines represent the theoretical results and the the solid lines represent the theoretical bound with the transmit power $\rho_k\rightarrow\infty$.