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Deep Learning-Driven Interference Perceptual Multi-Modulation for Full-Duplex Systems

Author

Listed:
  • Taehyoung Kim

    (School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea)

  • Gyuyeol Kong

    (Division of Mechanical and Electronics Engineering, Hansung University, Seoul 02876, Republic of Korea)

Abstract

In this paper, a novel data transmission scheme, interference perceptual multi-modulation (IP-MM), is proposed for full-duplex (FD) systems. In order to unlink the conventional uplink (UL) data transmission using a single modulation and coding scheme (MCS) over the entire assigned UL bandwidth, IP-MM enables the transmission of UL data channels based on multiple MCS levels, where a different MCS level is applied to each subband of UL transmission. In IP-MM, a deep convolutional neural network is used for MCS-level prediction for each UL subband by estimating the potential residual self-interference (SI) according to the downlink (DL) resource allocation pattern. In addition, a subband-based UL transmission procedure is introduced from a specification point of view to enable IP-MM-based UL transmission. The benefits of IP-MM are verified using simulations, and it is observed that IP-MM achieves approximately 20 % throughput gain compared to the conventional UL transmission scheme.

Suggested Citation

  • Taehyoung Kim & Gyuyeol Kong, 2024. "Deep Learning-Driven Interference Perceptual Multi-Modulation for Full-Duplex Systems," Mathematics, MDPI, vol. 12(10), pages 1-14, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1542-:d:1395230
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