Joint Training and Reflection Pattern Optimization for Non-Ideal RIS-Aided Multiuser Systems
<|reference_start|>Joint Training and Reflection Pattern Optimization for Non-Ideal RIS-Aided Multiuser Systems: 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.<|reference_end|>
@article{he2024joint,
    title={Joint Training and Reflection Pattern Optimization for Non-Ideal
  RIS-Aided Multiuser Systems},
    author={Zhenyao He, Jindan Xu, Hong Shen, Wei Xu, Chau Yuen, and Marco Di
  Renzo},
    journal={arXiv preprint arXiv:2403.19955},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19955},
    primaryClass={cs.IT math.IT}
}