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  • Intelligent_Radio_Resource_Management_in_Reconfigurable_IRS_enabled_NOMA_Networks

    Rights statement: This is the author’s version of a work that was accepted for publication in Physical Communication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Physical Communication, 53, 2022 DOI: 10.1016/j.phycom.2022.101744

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Intelligent radio resource management in reconfigurable IRS-enabled NOMA networks

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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  • S. Basharat
  • H. Pervaiz
  • S.A. Hassan
  • R.I. Ansari
  • H. Jung
  • K. Dev
  • G. Huang
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Article number101744
<mark>Journal publication date</mark>31/08/2022
<mark>Journal</mark>Physical Communication
Volume53
Number of pages13
Publication StatusPublished
Early online date26/05/22
<mark>Original language</mark>English

Abstract

Intelligent reflecting surfaces (IRSs) are anticipated to provide reconfigurable propagation environment for next generation communication systems. In this paper, we investigate a downlink IRS-aided multi-carrier (MC) non-orthogonal multiple access (NOMA) system, where the IRS is deployed to especially assist the blocked users to establish communication with the base station (BS). To maximize the system sum rate under network quality-of-service (QoS), rate fairness and successive interference cancellation (SIC) constraints, we formulate a problem for joint optimization of IRS elements, sub-channel assignment and power allocation. The formulated problem is mixed non-convex. Therefore, a novel three stage algorithm is proposed for the optimization of IRS elements, sub-channel assignment and power allocation. First, the IRS elements are optimized using the bisection method based iterative algorithm. Then, the sub-channel assignment problem is solved using one-to-one stable matching algorithm. Finally, the power allocation problem is solved under the given sub-channel and optimal number of IRS elements using Lagrangian dual-decomposition method based on Lagrangian multipliers. Moreover, in an effort to demonstrate the low-complexity of the proposed resource allocation scheme, we provide the complexity analysis of the proposed algorithms. The simulated results illustrate the various factors that impact the optimal number of IRS elements and the superiority of the proposed resource allocation approach in terms of network sum rate and user fairness. Furthermore, we analyze the proposed approach against a new performance metric called computational efficiency (CE).

Bibliographic note

This is the author’s version of a work that was accepted for publication in Physical Communication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Physical Communication, 53, 2022 DOI: 10.1016/j.phycom.2022.101744