Low-Overhead Channel Estimation for RIS-Aided Multi-Cell Networks in the Presence of Phase Quantization Errors

Deploying reconfigurable intelligent surfaces (RIS) is promising for enhancing the transmission reliability of wireless communications by controlling the wireless environment, in which the active beamforming at the base station and the passive beamforming at the RIS are jointly designed based on the acquisition of channel state information. Hence, channel estimation is crucial for RIS-aided systems. Due to the lack of active radio frequency chains at the RIS to process and transmit pilot sequences, only the cascaded twin-hop transmitter-RIS-receiver channel can be estimated, which results in extremely high pilot overhead, when a large number of RIS reflecting elements is used. As a remedy, we propose a channel estimation method relying on low pilot overhead, namely the Karhunen-Loève transformation based linear minimal mean square error (KL-LMMSE) estimator. This exploits the spatial correlation of the RIS-cascaded channels, for our multi-cell multiple-input and multiple-output RIS-aided systems. Furthermore, we extend our investigations to the effects of realistic phase quantization errors. Additionally, we derive the theoretical mean square error (MSE) of our proposed channel estimators verified by numerical simulations, and compare the results to various benchmark schemes. We show that the MSE performance of our proposed KL-LMMSE estimator is better than that of the state-of-the-art low-overhead channel estimators.