optimizing IRS-Assisted Uplink NOMA System for Power Constrained IoT Networks

This paper presents a novel approach for power-constrained internet of things (IoT) networks that employ non-orthogonal multiple access (NOMA) and are assisted by an intelligent reflecting surface (IRS) for uplink transmissions. The main objective of this work is to maximize the sum rate of power-constrained IoT networks by jointly designing the IRS phase shifts and the users’ transmit power allocation. The proposed solution optimizes the power allocation and phase shifts alternatively. We devise a novel approach to optimize the IRS phase shifts that is based on manifold optimization techniques. Specifically, the IRS phase shifts optimization problem is formulated and solved over the complex circle manifold. Our results show that the proposed method outperforms the widely used semi-definite relaxation (SDR) technique as higher sum rates with less power consumption can be achieved.