Two-Stage Power Allocation for IRS-Assisted Downlink NOMA: Intra-Group Optimization and Inter-Group Allocation

Two-Stage Power Allocation for IRS-Assisted Downlink NOMA: Intra-Group Optimization and Inter-Group Allocation

Non-Orthogonal Multiple Access (NOMA) has emerged as a key enabler for massive connectivity in next-generation networks, while Intelligent Reflecting Surfaces (IRS) provide a transformative approach to enhancing spectral efficiency by reconfiguring the wireless propagation environment. However, inte...

Full description

Saved in:
Bibliographic Details
Main Authors: Zaid Albataineh, Haythem Bany Salameh, Mohammad Al Bataineh
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Channel estimation
resource allocation
intelligent reflecting surface
channel conditions
non-orthogonal multiple access
user grouping
Online Access:https://ieeexplore.ieee.org/document/11003959/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Non-Orthogonal Multiple Access (NOMA) has emerged as a key enabler for massive connectivity in next-generation networks, while Intelligent Reflecting Surfaces (IRS) provide a transformative approach to enhancing spectral efficiency by reconfiguring the wireless propagation environment. However, integrating IRS with downlink NOMA presents significant challenges in power allocation, user grouping, and interference management, with existing methods often struggling to balance power efficiency, quality-of-service (QoS) guarantees, and computational scalability. This paper investigates a two-stage power allocation framework for IRS-assisted downlink NOMA. The first stage performs intra-group optimization, jointly adjusting user-level power allocation and IRS phase shifts to minimize power consumption while meeting QoS requirements based on an iterative optimization procedure. The second stage optimizes inter-group, allocating power across user groups to maximize the number of served users based on a sequential fixing programming procedure. A detailed complexity analysis demonstrates the computational efficiency of the framework, enabling real-time deployment in dynamic networks. The simulation results confirm that the proposed approach achieves superior sum rate performance while minimizing total power consumption and maintaining QoS guarantees.
ISSN:2169-3536

Similar Items