He, Z., Xu, J., Shen, H., Xu, W., Yuen, C., & Renzo, M. D. (2024). Joint Training and Reflection Pattern Optimization for Non-Ideal RIS-Aided Multiuser Systems. IEEE Transactions on Communications, 72(9), 5735–5751. https://doi.org/10.1109/tcomm.2024.3383107
Abstract:
Reconfigurable intelligent surface (RIS) is a promising
technique to improve the performance of future wireless
communication systems at low energy consumption. To reap the
potential benefits of RIS-aided beamforming, it is vital to enhance
the accuracy of channel estimation. In this paper, we consider
an RIS-aided multiuser system with non-ideal reflecting elements,
each of which has a phase-dependent reflecting amplitude, and
we aim to minimize the mean-squared error (MSE) of the
channel estimation by jointly optimizing the training signals at
the user equipments (UEs) and the reflection pattern at the RIS.
As examples the least squares (LS) and linear minimum MSE
(LMMSE) estimators are considered. The considered problems
do not admit simple solution mainly due to the complicated
constraints pertaining to the non-ideal RIS reflecting elements.
As far as the LS criterion is concerned, we tackle this difficulty by
first proving the optimality of orthogonal training symbols and
then propose a majorization-minimization (MM)-based iterative
method to design the reflection pattern, where a semi-closed
form solution is obtained in each iteration. As for the LMMSE
criterion, we address the joint training and reflection pattern
optimization problem with an MM-based alternating algorithm,
where a closed-form solution to the training symbols and a semi-closed form solution to the RIS reflecting coefficients are
derived, respectively. Furthermore, an acceleration scheme is
proposed to improve the convergence rate of the proposed MM
algorithms. Finally, simulation results demonstrate the performance
advantages of our proposed joint training and reflection
pattern designs.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Key R&D Program of China - NA
Grant Reference no. : 2021YFB2900300
This research / project is supported by the NSFC - NA
Grant Reference no. : 62211530108
This research / project is supported by the Fundamental Research Funds for the Central Universities - NA
Grant Reference no. : 2242022K60002, 2242023K5003
This research / project is supported by the ZTE Industry-University- Institute Cooperation Funds - NA
Grant Reference no. : IA20240319003
This research / project is supported by the Ministry of Education, Singapore - Academic Research Fund Tier 2
Grant Reference no. : MOE-T2EP50220-0019
This research / project is supported by the Science and Engineering Research Council of A*STAR (Agency for Science, Technology and Research) Singapore - Manufacturing, Trade, and Connectivity Programmatic Fund
Grant Reference no. : M22L1b0110
This research / project is supported by the European Commission - Horizon Europe project COVER
Grant Reference no. : 101086228
This research / project is supported by the European Commission - Horizon Europe project UNITE
Grant Reference no. : 101129618
This research / project is supported by the European Commission - Horizon Europe project INSTINCT
Grant Reference no. : 101139161
This research / project is supported by the Agence Nationale de la Recherche (ANR) - France 2030 project ANR-PEPR Networks of the Future
Grant Reference no. : NF-PERSEUS 22-PEFT-004
This research / project is supported by the Agence Nationale de la Recherche (ANR) - CHIST-ERA project PASSIONATE
Grant Reference no. : CHIST-ERA-22-WAI-04, ANR-23-CHR4-0003-01