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

Taehyoung Kim and Gyuyeol Kong ()
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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

Mathematics, 2024, vol. 12, issue 10, 1-14

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.

Keywords: convolutional neural network; full-duplex; IP-MM; MCS (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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