Intelligent Reflecting Surfaces (IRS)-Enhanced Cooperative NOMA: A Contemporary Review

Abstract

The integration of intelligent reflecting surfaces (IRS) into cooperative non-orthogonal multiple access (NOMA) systems revolutionizes wireless networks by enhancing signal strength, mitigating interference, and optimizing spectral efficiency. The cooperative NOMA (CNOMA) framework, empowered by IRS technology, further promises enhanced performance, robustness, and scalability for next-generation wireless networks as compared to NOMA only systems. This paper explores the synergy between IRS and NOMA to leverage cooperative techniques for superior wireless system design. Fundamental principles, technological advancements, and potential applications of IRS-assisted CNOMA systems are discussed, highlighting existing works. Both underlay and overlay NOMA principles are examined in conjunction with IRS in the paper. Spatial modulation-aided CNOMA is explored for multiple-input multiple-output (MIMO) systems, along with its advantages and practical challenges. Additionally, the paper discusses fundamental principles and technological advancements of IRS-assisted CNOMA systems, emphasizing solutions to potential challenges and the role of machine learning (ML)/deep learning (DL) in resource optimization like transmit power and IRS phase settings. Simulation results are presented to highlight the benefits of IRS-aided CNOMA system design. Finally, the paper outlines future directions and potential research topics in IRS-aided CNOMA.

Keywords

• Intelligent reflecting surfaces (IRS)
• cooperative-NOMA (CNOMA)
• bit error performance
• 5G and beyond
• machine learning
• MIMO