Energy-Efficient Power Control and Beamforming for Reconfigurable Intelligent Surface-Aided Uplink IoT Networks

TL;DR

This paper introduces a joint optimization method using Riemannian conjugate gradient techniques to minimize uplink power in RIS-assisted IoT networks, significantly improving energy efficiency.

Contribution

It proposes a novel Riemannian conjugate gradient-based joint optimization scheme for RIS phase shifts and beamforming, enhancing power efficiency in IoT networks.

Findings

Achieves 94% reduction in uplink transmit power.

Effectively converts a nonconvex problem into an unconstrained one.

Demonstrates superior performance over conventional schemes.

Abstract

Recently, reconfigurable intelligent surface (RIS), a planar metasurface consisting of a large number of low-cost reflecting elements, has received much attention due to its ability to improve both the spectrum and energy efficiencies by reconfiguring the wireless propagation environment. In this paper, we propose a RIS phase shift and BS beamforming optimization technique that minimizes the uplink transmit power of a RIS-aided IoT network. Key idea of the proposed scheme, referred to as Riemannian conjugate gradient-based joint optimization (RCG-JO), is to jointly optimize the RIS phase shifts and the BS beamforming vectors using the Riemannian conjugate gradient technique. By exploiting the product Riemannian manifold structure of the sets of unit-modulus phase shifts and unit-norm beamforming vectors, we convert the nonconvex uplink power minimization problem into the unconstrained problem and then find out the optimal solution on the product Riemannian manifold. From the performance analysis and numerical evaluations, we demonstrate that the proposed RCG-JO technique achieves $94\%$ reduction of the uplink transmit power over the conventional scheme without RIS.