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Throughput Enhancement for NOMA Systems Using Intelligent Reflecting Surfaces

Raed Alhamad1,*, Hatem Boujemaa2

1 Information Technology Department, Saudi Electronic University, Riaydh, Saudi Arabia
2 UCAR-SUPCOM-COSIM, Ariana, 2083, Tunisia

* Corresponding Author: Raed Alhamad. Email: email

Computers, Materials & Continua 2022, 73(3), 5233-5244. https://doi.org/10.32604/cmc.2022.030793

Abstract

In this article, we optimize the powers associated to Non Orthogonal Multiple Access (NOMA) users, sensing and harvesting duration for Cognitive Radio Networks (CRN). The secondary source harvests energy from node A signal. Then, it senses the channel to detect primary source. Then, the secondary source transmits a signal that is reflected by Intelligent Reflecting Surfaces (IRS) so that all reflections have a zero phase at any user. A set Ii of reflectors are associated to user Ui. The use of M = Mi = 512, 256, 128, 64, 32, 16, 8 reflectors per user offers 45, 42, 39, 36, 33, 30, 27 dB gain vs. the absence of IRS. We also suggest the use of IRS in energy harvesting. The use P = 8 reflectors for energy harvesting and M = Mi = 8 reflectors per user for data communications offers 7 and 38 dB gain vs. one IRS M = Mi = 8 and the absence of IRS. The use of P = 16 reflectors for energy harvesting and M = Mi = 8 reflectors per user for data communications offers 9 and 42 dB gain vs. one IRS M = Mi = 8 and the absence of IRS.

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Cite This Article

APA Style
Alhamad, R., Boujemaa, H. (2022). Throughput enhancement for NOMA systems using intelligent reflecting surfaces. Computers, Materials & Continua, 73(3), 5233-5244. https://doi.org/10.32604/cmc.2022.030793
Vancouver Style
Alhamad R, Boujemaa H. Throughput enhancement for NOMA systems using intelligent reflecting surfaces. Comput Mater Contin. 2022;73(3):5233-5244 https://doi.org/10.32604/cmc.2022.030793
IEEE Style
R. Alhamad and H. Boujemaa, "Throughput Enhancement for NOMA Systems Using Intelligent Reflecting Surfaces," Comput. Mater. Contin., vol. 73, no. 3, pp. 5233-5244. 2022. https://doi.org/10.32604/cmc.2022.030793



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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