Optimal phase shift design for fair allocation in RIS aided uplink network using statistical CSI

Abstract

Reconfigurable intelligent surface (RIS) can be crucial in next-generation communication systems.However, designing the RIS phases according to the instantaneous channel state information (CSI) can be challenging in practice due to the short coherent time of the channel. In this regard, we propose a novel algorithm based on the channel statistics of massive multiple input multiple output systems rather than the CSI. The beamforming at the base station (BS), power allocation of the users, and phase shifts at the RIS elements are optimized to maximize the minimum signal to interference and noise ratio (SINR), guaranteeing fair operation among various users. In particular, we design the RIS phases by leveraging the asymptotic deterministic equivalent of the minimum SINR that depends only on the channel statistics. This significantly reduces the computational complexity and the amount of controlling data between the BS and RIS for updating the phases. This setup is also useful for electromagnetic fields (EMF)-aware systems with constraints on the maximum user's exposure to EMF. The numerical results show that the proposed algorithms achieve more than 100% gain in terms of minimum SINR, compared to a system with random RIS phase shifts, with 40 RIS elements, 20 antennas at the BS and 10 users, respectively.

Joint Uplink and Downlink EMF Exposure: Performance Analysis and Design Insights

“…In [28], the RIS phases were optimized to minimize the total uplink exposure of users. On the other hand, the work in [29] and [30] considered designing the RIS phases to minimize the maximum exposure (min-max problem) with instantaneous and statistical channel state information, respectively. The authors in [31] applied probabilistic shaping to minimize the average EMF exposure while ensuring a target throughput.…”

MU-Massive MIMO With Multiple RISs: SINR Maximization and Asymptotic Analysis

“…Several studies have been conducted on solving the challenges of beamforming and phase shift design in an RISaided multi-user MIMO scenario with different objectives [4]- [14] such as maximization of the sum rate [4], [5], spectral efficiency [6] and energy efficiency [7], [8] or minimization Fig. 1: The system model of the transmission power [9], max-min SINR problem [10], [11], and the exposure to electromagnetic fields [12], [13]. However, the proposed solutions involve intricate algorithms and straightforward designs are not presented in the literature.…”

Instantaneous Channel Oblivious Phase Shift Design for an IRS-Assisted SIMO System with Quantized Phase Shift

“…They alternatively used geometric programming and the matrix-lifting method for power allocation and phase shift design at IRS such that the minimum SINR is maximized, respectively. In [23], authors worked on a similar system model as in [24], and they derived an equation for solving the asymptotic minimum SINR using the tools from random matrix theory. They employed alternating optimization to solve for the beamforming vectors at BS, power allocation of users, and phase shift at IRS to maximize the minimum SINR, considering the availability of continuous phase shift at IRS.…”

“…The statistical parameterbased phase shift design approach will require lesser feedback between BS and IRS controller when compared with an instantaneous CSI-based scheme. The works like [22], [23], [25], [26] consider the statistical CSI for designing the phase shift at the IRS with the assumption that any continuous phase value can be assigned to IRS elements. However, the IRS is envisioned to be a low-cost passive device with a high number of reflective elements; hence the availability of infinite resolution phase shift at elements of the IRS is not practical due to hardware limitations [2], [16], [27], there can be only a finite number of discrete values among which the IRS has to select the phase shift.…”