Joint Training and Reflection Pattern Optimization for Non-Ideal RIS-Aided Multiuser Systems
IEEE Transactions on Communications(2024)
摘要
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.
更多查看译文
关键词
Reconfigurable intelligent surface (RIS),channel estimation,least squares (LS),linear minimum mean-squared error (LMMSE),reflection pattern,majorization-minimization (MM)
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要