IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i15p5668-d880363.html
   My bibliography  Save this article

Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels

Author

Listed:
  • Mohamed Hassan

    (Department of Wireless Communication, Lovely Professional University, Phagwara 144001, Punjab, India)

  • Manwinder Singh

    (Department of Wireless Communication, Lovely Professional University, Phagwara 144001, Punjab, India)

  • Khalid Hamid

    (Department Communication Systems Engineering, University of Science & Technology, Omdurman P.O. Box 30, Sudan)

  • Rashid Saeed

    (Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Maha Abdelhaq

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Raed Alsaqour

    (Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, Riyadh 93499, Saudi Arabia)

Abstract

The integration of multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) technologies is a hybrid technology that overcomes a myriad of problems in the 5G cellular system and beyond, including massive connectivity, low latency, and high dependability. The goal of this paper is to improve and reassess the bit error rate (BER), spectrum efficiency (SE) of the downlink (DL), average capacity rate, and outage probability (OP) of the uplink (UL) in a 5G network using MIMO. The proposed model utilizes QPSK modulation, four users with different power location coefficients, SNR, transmit power, and two contrasting bandwidths 80 and 200 MHz under selective frequency Rayleigh fading channels. The proposed model’s performance is evaluated using the MATLAB software program. The DL results found that the BER and SE against transmitted power showed the MIMO-NOMA enhanced the BER performance for the best user U4 from 10 −1.7 to 10 −5.2 at 80 MHz bandwidth (BW), and from 10 −1.5 to 10 −5 at 200 MHz for transmitting power of 40 dBm. In contrast, the SE performance for the best user U4 is enhanced from 24 × 10 −3 to 25 × 10 −3 bits/second/Hz at 80 MHz BW and from 19.8 × 10 −3 to 20 × 10 −3 bps/Hz at 200 MHz BW. Although the outcomes for the UL were obtained in terms of average capacity rate and OP versus SNR at 80, and 200 MHz BW, the MIMO-NOMA result showed that the average capacity rate for the best user U4 performance improves by 12 bps/Hz for 1 dB SNR and the OP is reduced by 15 × 10 −3 for 80 MHz BW and by 12 × 10 −3 for 200 MHz BW at an SNR of 0.17 dB. As the BW increased the BER, the average capacity rate increased while the SE and OP decreased. For both DL/UL NOMA with and without MIMO, closed-form expressions for BER, SE, average capacity rate, and OP were obtained. All users’ performance, even those whose connections were affected by interference or Rayleigh fading channels significantly improved, when MIMO-NOMA was implemented.

Suggested Citation

  • Mohamed Hassan & Manwinder Singh & Khalid Hamid & Rashid Saeed & Maha Abdelhaq & Raed Alsaqour, 2022. "Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels," Energies, MDPI, vol. 15(15), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5668-:d:880363
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/15/5668/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/15/5668/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bilal Ur Rehman & Mohammad Inayatullah Babar & Arbab Waheed Ahmad & Hesham Alhumyani & Gamil Abdel Azim & Rashid A. Saeed & Sayed Abdel Khalek, 2021. "Joint power control and user grouping for uplink power domain non-orthogonal multiple access," International Journal of Distributed Sensor Networks, , vol. 17(12), pages 15501477211, December.
    2. Hanlei Sun & Jianrui Sun & Kun Zhao & Licheng Wang & Kai Wang & Mohammad Yaghoub Abdollahzadeh Jamalabadi, 2022. "Data-Driven ICA-Bi-LSTM-Combined Lithium Battery SOH Estimation," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, March.
    3. Yu Hua & Na Wang & Keyou Zhao, 2021. "Simultaneous Unknown Input and State Estimation for the Linear System with a Rank-Deficient Distribution Matrix," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    2. Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    3. Dai, Houde & Wang, Jiaxin & Huang, Yiyang & Lai, Yuan & Zhu, Liqi, 2024. "Lightweight state-of-health estimation of lithium-ion batteries based on statistical feature optimization," Renewable Energy, Elsevier, vol. 222(C).
    4. Li, Dezhi & Li, Shuo & Zhang, Shubo & Sun, Jianrui & Wang, Licheng & Wang, Kai, 2022. "Aging state prediction for supercapacitors based on heuristic kalman filter optimization extreme learning machine," Energy, Elsevier, vol. 250(C).
    5. Panpan Hu & W. F. Tang & C. H. Li & Shu-Lun Mak & C. Y. Li & C. C. Lee, 2023. "Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network," Energies, MDPI, vol. 16(14), pages 1-19, July.
    6. Mahendiran T. Vellingiri & Ibrahim M. Mehedi & Thangam Palaniswamy, 2022. "A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles," Mathematics, MDPI, vol. 10(2), pages 1-15, January.
    7. Xinwei Sun & Yang Zhang & Yongcheng Zhang & Licheng Wang & Kai Wang, 2023. "Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(15), pages 1-19, July.
    8. Youcef Himri & Shafiqur Rehman & Ali Mostafaeipour & Saliha Himri & Adel Mellit & Mustapha Merzouk & Nachida Kasbadji Merzouk, 2022. "Overview of the Role of Energy Resources in Algeria’s Energy Transition," Energies, MDPI, vol. 15(13), pages 1-26, June.
    9. Li, Feng & Zuo, Wei & Zhou, Kun & Li, Qingqing & Huang, Yuhan & Zhang, Guangde, 2024. "State-of-charge estimation of lithium-ion battery based on second order resistor-capacitance circuit-PSO-TCN model," Energy, Elsevier, vol. 289(C).
    10. Liu, Chunli & Li, Qiang & Wang, Kai, 2021. "State-of-charge estimation and remaining useful life prediction of supercapacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    11. Khac Huan Su & Jaeyun Yim & Wonhee Kim & Youngwoo Lee, 2022. "Lyapunov-Based Controller Using Nonlinear Observer for Planar Motors," Mathematics, MDPI, vol. 10(13), pages 1-18, June.
    12. Zhang, Chaolong & Luo, Laijin & Yang, Zhong & Du, Bolun & Zhou, Ziheng & Wu, Ji & Chen, Liping, 2024. "Flexible method for estimating the state of health of lithium-ion batteries using partial charging segments," Energy, Elsevier, vol. 295(C).
    13. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures," Renewable Energy, Elsevier, vol. 198(C), pages 1328-1340.
    14. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Dimitrios Kontogiannis & Ioannis P. Panapakidis & Lefteri H. Tsoukalas, 2022. "Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting," Energies, MDPI, vol. 15(4), pages 1-14, February.
    15. Shigui Dong & Na Wang & Xueyan Wang & Zihao Lu, 2023. "Extended Recursive Three-Step Filter for Linear Discrete-Time Systems with Dual-Unknown Inputs," Energies, MDPI, vol. 16(15), pages 1-18, July.
    16. Mei Zhang & Wanli Chen & Jun Yin & Tao Feng, 2022. "Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost," Energies, MDPI, vol. 15(16), pages 1-18, August.
    17. Chenqiang Luo & Zhendong Zhang & Shunliang Zhu & Yongying Li, 2023. "State-of-Health Prediction of Lithium-Ion Batteries Based on Diffusion Model with Transfer Learning," Energies, MDPI, vol. 16(9), pages 1-14, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5668-:d:880363. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.