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Power Beacon and NOMA-Assisted Cooperative IoT Networks With Co-Channel Interference: Performance Analysis and Deep Learning Evaluation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255015" target="_blank" >RIV/61989100:27240/24:10255015 - isvavai.cz</a>

  • Alternative codes found

    RIV/61989100:27740/24:10255015

  • Result on the web

    <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10321692" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10321692</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/TMC.2023.3333764" target="_blank" >10.1109/TMC.2023.3333764</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Power Beacon and NOMA-Assisted Cooperative IoT Networks With Co-Channel Interference: Performance Analysis and Deep Learning Evaluation

  • Original language description

    This study investigates a two-way relaying non-orthogonal multiple access (TWR-NOMA) enabled Internet-of-Things (IoT) network, in which two NOMA users communicate via an IoT access point (IAP) relay using a decode-and-forward (DF) protocol. A power beacon (PB) is used to power the IAP to address the IAP&apos;s limited lifetime due to energy constraints. Since co-channel interference (CCI) is inevitable in IoT systems, this effect is also studied in the proposed system to improve practicality. Based on the proposed system model, the closed-form equations for the exact and asymptotic outage probability (OP) and ergodic data (ED) of the NOMA users&apos; signals are first derived to describe the performance of TWR-NOMA systems. The system&apos;s diversity order and throughput are then evaluated according to the derived results. To further improve the system&apos;s performance, a low-complexity strategy 2D golden section search (GSS) is performed, subject to power allocation (PA) and time-switching (TS) factors, to optimize the outage performance. Finally, a deep learning design with minimal computing complexity and precision OP prediction is established for a real-time IoT network configuration. The numerical results are discussed and analyzed in terms of the effects of the CCI, the TS ratio, the PA factor, the fading parameter on the OP, system throughput, and ED.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20203 - Telecommunications

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2024

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    IEEE transactions on mobile computing

  • ISSN

    1536-1233

  • e-ISSN

    1558-0660

  • Volume of the periodical

    23

  • Issue of the periodical within the volume

    6

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    14

  • Pages from-to

    7270-7283

  • UT code for WoS article

    001216462000025

  • EID of the result in the Scopus database