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Sum Rate Optimization of IRS-Aided Uplink Muliantenna NOMA with Practical Reflection

Version 3 2024-06-19, 13:45
Version 2 2024-06-05, 01:21
Version 1 2022-09-29, 22:36
journal contribution
posted on 2024-06-19, 13:45 authored by J Choi, L Cantos, Jinho ChoiJinho Choi, YH Kim
Recently, intelligent reflecting surfaces (IRSs) have drawn huge attention as a promising solution for 6G networks to enhance diverse performance metrics in a cost-effective way. For massive connectivity toward a higher spectral efficiency, we address an intelligent reflecting surface (IRS) to an uplink nonorthogonal multiple access (NOMA) network supported by a multiantenna receiver. We maximize the sum rate of the IRS-aided NOMA network by optimizing the IRS reflection pattern under unit modulus and practical reflection. For a moderate-sized IRS, we obtain an upper bound on the optimal sum rate by solving a determinant maximization (max-det) problem after rank relaxation, which also leads to a feasible solution through Gaussian randomization. For a large number of IRS elements, we apply the iterative algorithms relying on the gradient, such as Broyden–Fletcher–Goldfarb–Shanno (BFGS) and limited-memory BFGS algorithms for which the gradient of the sum rate is derived in a computationally efficient form. The results show that the max-det approach provides a near-optimal performance under unit modulus reflection, while the gradient-based iterative algorithms exhibit merits in performance and complexity for a large-sized IRS with practical reflection.

History

Journal

Sensors

Volume

22

Article number

ARTN 4449

Location

Switzerland

ISSN

1424-8220

eISSN

1424-8220

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Issue

12

Publisher

MDPI