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    Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation

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    Type
    Article
    Authors
    Albinsaid, Hasan
    Singh, Keshav
    Biswas, Sudip
    Li, Chih-Peng
    Alouini, Mohamed-Slim cc
    KAUST Department
    Communication Theory Lab
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/664608
    
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    Abstract
    Generalized Spatial Modulation (GSM) is being considered for high capacity and energy-efficient networks of the future. However, signal detection due to inter channel interference among the active antennas is a challenge in GSM systems and is the focus of this paper. Specifically, we explore the feasibility of using deep neural networks (DNN) for signal detection in GSM. In particular, we propose a block DNN (BDNN) based architecture, where the active antennas and their transmitted constellation symbols are detected by smaller sub- DNNs. After N-ordinary DNN detection, the Euclidean distancebased soft constellation algorithm is implemented. The proposed B-DNN detector achieves a BER performance that is superior to traditional block zero-forcing (B-ZF) and block minimum mean-squared error (B-MMSE) detection schemes and similar to that of classical maximum likelihood (ML) detector. Further, the proposed method requires less computation time and is more accurate than alternative conventional numerical methods.
    Citation
    Albinsaid, H., Singh, K., Biswas, S., Li, C.-P., & Alouini, M.-S. (2020). Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation. IEEE Communications Letters, 1–1. doi:10.1109/lcomm.2020.3015810
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Communications Letters
    DOI
    10.1109/LCOMM.2020.3015810
    arXiv
    2008.03612
    Additional Links
    https://ieeexplore.ieee.org/document/9165095/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9165095
    http://arxiv.org/pdf/2008.03612
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    • [Software]
      Albinsaid, H., Keshav Singh, Sudip Biswas, Chih-Peng Li, & Mohamed-Slim Alouini. (2020). Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation (Version 2.0) [Computer software]. Code Ocean. https://doi.org/10.24433/CO.3589818.V2. DOI: 10.24433/co.3589818.v2 Handle: 10754/667586
    ae974a485f413a2113503eed53cd6c53
    10.1109/LCOMM.2020.3015810
    Scopus Count
    Collections
    Articles; Electrical and Computer Engineering Program; Communication Theory Lab; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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