Intelligent Reflecting Surface-Assisted Multiple Access With User Pairing: NOMA or OMA?

The integration of intelligent reflecting surface (IRS) to multiple access networks is a cost-effective solution for boosting spectrum/energy efficiency and enlarging network coverage/connections. However, due to the new capability of IRS in reconfiguring the wireless propagation channels, it is fundamentally unknown which multiple access scheme is superior in the IRS-assisted wireless network. In this letter, we pursue a theoretical performance comparison between non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) in the IRS-assisted downlink communication, for which the transmit power minimization problems are formulated under the discrete unit-modulus reflection constraint on each IRS element. We analyze the minimum transmit powers required by different multiple access schemes and compare them numerically, which turn out to not fully comply with the stereotyped superiority of NOMA over OMA in conventional systems without IRS. Moreover, to avoid the exponential complexity of the brute-force search for the optimal discrete IRS phase shifts, we propose a low-complexity solution to achieve near-optimal performance.

I. Introduction

Intelligent reflecting surface (IRS) has recently attracted growing attention and is envisioned as an innovative technology for the beyond fifth-generation (B5G) communication system, due to its potential of achieving significant improvement in communication coverage, throughput, and energy efficiency [1]–[3]. Specifically, IRS is a planar meta-surface composed of a large number of reconfigurable passive elements, which are attached with a smart controller to enable dynamic adjustment on the signal reflections for different purposes, such as signal power enhancement and interference suppression. In particular, compared to conventional techniques such as active relaying/beamforming, IRS not only reflects signals in a full-duplex and noise-free manner without incurring self-interference, but also greatly saves energy consumption and hardware/deployment cost by using lightweight passive components only [1], [3].