Phase Selection and Analysis for Multi-frequency Multi-user RIS Systems Employing Subsurfaces

TL;DR

This paper analyzes the performance of a reconfigurable intelligent surface (RIS) aided system where the RIS is divided into subsurfaces and quantifies the complexity-performance trade-off for the new design relative to multi-user approaches.

Abstract

In this paper, we analyse the performance of a reconfigurable intelligent surface (RIS) aided system where the RIS is divided into subsurfaces. Each subsurface is designed specifically for one user, who is served on their own frequency band. The other subsurfaces (those not designed for this user) provide additional uncontrolled scattering. A new subsurface RIS design is developed based on the optimal single-user design for a pure line-of-sight (LoS) base station (BS) to RIS channel. This is also extended to arbitrary BS-RIS channels. For our method, exact closed form solutions for the mean SNR and a mean rate upper bound are derived for the BS-RIS LoS scenario. For each user, the designed subsurface performs optimally in LoS conditions and is remarkably robust to non-LoS conditions. The system design drives down complexity to extremely low levels, reducing RIS design and receiver processing complexity and reducing the channel estimation requirements. We also quantify the complexity-performance trade-off for the new design relative to multi-user approaches.

Figures (7)

• Figure 1: System model showing channels for UE 1 in band 1.
• Figure 2: Mean SNR comparison between the $\mathbb{E}[\mathrm{SNR}_1]$ from (\ref{['meansnr']}), the ESD method from Sec. \ref{['sec:Phi_NLoS']} and the IPA optimal.
• Figure 3: Simulated CDFs for $N = K\times 128$ for $K=\{1,2,4\}$.
• Figure 4: Mean rate vs upper bound comparison for user 1 with $K$ = {1,2,4} total users, $N=128$.
• Figure 5: A comparison of ${R}_\mathrm{sum}$ for the subsurface, TMSE and random RIS phase selection methods for a range of $K$.