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    Power Allocation for Relayed OFDM with Index Modulation Assisted by Artificial Neural Network

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    power allocation.pdf
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    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Zhou, Jiusi cc
    Dang, Shuping
    Shihada, Basem cc
    Alouini, Mohamed-Slim cc
    KAUST Department
    Communication Theory Lab
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering
    Electrical Engineering Program
    Networks Laboratory (NetLab)
    Date
    2020-10-16
    Preprint Posting Date
    2020-10-24
    Online Publication Date
    2020-10-16
    Print Publication Date
    2021-02
    Permanent link to this record
    http://hdl.handle.net/10754/665614
    
    Metadata
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    Abstract
    In this letter, we propose a power allocation scheme for relayed orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. The proposed power allocation scheme replies on artificial neural network (ANN) and deep learning to allocate transmit power among various subcarriers at the source and relay nodes. The objective of the power allocation scheme is to minimize the overall transmit power under a set of constraints. Without loss of generality, we assume all subcarriers at source and relay nodes are independently distributed with different statistical distribution parameters. The relay node adopts the fixed-gain amplify-and-forward (FG AF) relaying protocol. We employ the adaptive moment estimation method (Adam) to implement back-propagation learning and simulate the proposed power allocation scheme. The analytical and simulation results show that the proposed power allocation scheme is able to provide comparable performance as the optimal solution but with lower complexity.
    Citation
    Zhou, J., Dang, S., Shihada, B., & Alouini, M.-S. (2020). Power Allocation for Relayed OFDM with Index Modulation Assisted by Artificial Neural Network. IEEE Wireless Communications Letters, 1–1. doi:10.1109/lwc.2020.3031638
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Wireless Communications Letters
    DOI
    10.1109/LWC.2020.3031638
    arXiv
    2010.12959
    Additional Links
    https://ieeexplore.ieee.org/document/9226618/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9226618
    ae974a485f413a2113503eed53cd6c53
    10.1109/LWC.2020.3031638
    Scopus Count
    Collections
    Articles; Computer Science Program; Electrical and Computer Engineering Program; Communication Theory Lab; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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