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A Deep Learning-Driven Solution to Limited-Feedback MIMO Relaying Systems

Author

Listed:
  • Kwadwo Boateng Ofori-Amanfo

    (Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
    Current address: Smart Energy and Intelligence Systems Lab (Smart E&I Lab), Department of Industrial Engineering, Kumoh National Institute of Technology, 61 Daehak-ro (Yangho-dong), Gumi 39177, Republic of Korea.)

  • Bridget Durowaa Antwi-Boasiako

    (Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Republic of Korea)

  • Prince Anokye

    (Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
    Lab-STICC, IMT-Atlantique, 29238 Brest, France)

  • Suho Shin

    (Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Republic of Korea)

  • Kyoung-Jae Lee

    (Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Republic of Korea)

Abstract

In this work, we investigate a new design strategy for the implementation of a deep neural network (DNN)-based limited-feedback relay system by using conventional filters to acquire training data in order to jointly solve the issues of quantization and feedback. We aim to maximize the effective channel gain to reduce the symbol error rate (SER). By harnessing binary feedback information from the implemented DNNs together with efficient beamforming vectors, a novel approach to the resulting problem is presented. We compare our proposed system to a Grassmannian codebook system to show that our system outperforms its benchmark in terms of SER.

Suggested Citation

  • Kwadwo Boateng Ofori-Amanfo & Bridget Durowaa Antwi-Boasiako & Prince Anokye & Suho Shin & Kyoung-Jae Lee, 2025. "A Deep Learning-Driven Solution to Limited-Feedback MIMO Relaying Systems," Mathematics, MDPI, vol. 13(14), pages 1-13, July.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:14:p:2246-:d:1699339
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