IRS-Assisted Massive MIMO-NOMA Networks: Exploiting Wave Polarization
Arthur S. de Sena, Pedro H. J. Nardelli, Daniel B. da Costa, F. Rafael, M. Lima, Liang Yang, Petar Popovski, Zhiguo Ding, Constantinos B. Papadias

TL;DR
This paper proposes a dual-polarized IRS-assisted massive MIMO-NOMA system that exploits polarization diversity to improve user multiplexing and mitigate SIC errors, outperforming traditional systems.
Contribution
It introduces a novel polarization domain multiplexing strategy with optimized IRSs, providing analytical rate expressions and demonstrating performance gains over conventional methods.
Findings
IRS size significantly improves system performance
Proposed scheme outperforms MIMO-NOMA and MIMO-OMA systems
Dual-polarized IRSs enable beneficial cross-polar transmissions
Abstract
A dual-polarized intelligent reflecting surface (IRS) can contribute to a better multiplexing of interfering wireless users. In this paper, we use this feature to improve the performance of dual-polarized massive multiple-input multiple-output (MIMO) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC). By considering the downlink of a multi-cluster scenario, the IRSs assist the base station (BS) to multiplex subsets of users in the polarization domain. Our novel strategy alleviates the impact of imperfect SIC and enables users to exploit polarization diversity with near-zero inter-subset interference. To this end, the IRSs are optimized to mitigate transmissions originated at the BS from the interfering polarization. The formulated optimization is transformed into quadratic constrained quadratic sub-problems, which makes it possible to…
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