TY - GEN
T1 - Low-Complexity Channel Estimation for RIS-Assisted Multi-User Wireless Communications
AU - Li, Qingchao
AU - El-Hajjar, Mohammed
AU - Hemadeh, Ibrahim
AU - Mestrah, Yasser
AU - Shojaeifard, Arman
AU - Hanzo, Lajos
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Reconfigurable intelligent surfaces
KW - channel estimation
KW - linear minimum mean square error
KW - spatial channel correlation
UR - https://www.scopus.com/pages/publications/105018460526
U2 - 10.1109/ICC52391.2025.11160898
DO - 10.1109/ICC52391.2025.11160898
M3 - 会议稿件
AN - SCOPUS:105018460526
T3 - IEEE International Conference on Communications
SP - 6179
EP - 6184
BT - ICC 2025 - IEEE International Conference on Communications
A2 - Valenti, Matthew
A2 - Reed, David
A2 - Torres, Melissa
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE International Conference on Communications, ICC 2025
Y2 - 8 June 2025 through 12 June 2025
ER -