Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint Beamforming Optimization
Xidong Mu, Yuanwei Liu, Li Guo, Jiaru Lin, Naofal, Al-Dhahir

TL;DR
This paper develops joint beamforming algorithms for IRS-assisted NOMA systems to maximize sum rate, considering ideal and non-ideal IRS models, and demonstrates significant performance improvements over traditional methods.
Contribution
It introduces novel optimization algorithms for joint active and passive beamforming in IRS-aided NOMA networks under various IRS assumptions.
Findings
IRS deployment significantly boosts sum rate.
3-bit phase shifters nearly match ideal IRS performance.
IRS-aided NOMA outperforms IRS-aided OMA systems.
Abstract
This paper investigates a downlink multiple-input single-output intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) system, where a base station (BS) serves multiple users with the aid of IRSs. Our goal is to maximize the sum rate of all users by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, subject to successive interference cancellation decoding rate conditions and IRS reflecting elements constraints. In term of the characteristics of reflection amplitudes and phase shifts, we consider ideal and non-ideal IRS assumptions. To tackle the formulated non-convex problems, we propose efficient algorithms by invoking alternating optimization, which design the active beamforming and passive beamforming alternately. For the ideal IRS scenario, the two subproblems are solved by invoking the successive convex…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
