Deep Learning-Aided Multicarrier Systems

Thien Van Luong, Youngwook Ko, Michail Matthaiou, Ngo Anh Vien, Minh-Tuan Le, Vu-Duc Ngo

Research output: Contribution to journalArticlepeer-review


This paper proposes a deep learning (DL)-aided
multicarrier (MC) system operating on fading channels, where
both modulation and demodulation blocks are modeled by deep
neural networks (DNNs), regarded as the encoder and decoder
of an autoencoder (AE) architecture, respectively. Unlike existing
AE-based systems, which incorporate domain knowledge of a
channel equalizer to suppress the effects of wireless channels, the
proposed scheme, termed as MC-AE, directly feeds the decoder
with the channel state information and received signal, which are
then processed in a fully data-driven manner. This new approach
enables MC-AE to jointly learn the encoder and decoder to
optimize the diversity and coding gains over fading channels. In
particular, the block error rate of MC-AE is analyzed to show its
higher performance gains than existing hand-crafted baselines,
such as various recent index modulation-based MC schemes. We
then extend MC-AE to multiuser scenarios, wherein the resultant
system is termed as MU-MC-AE. Accordingly, two novel DNN
structures for uplink and downlink MU-MC-AE transmissions
are proposed, along with a novel cost function that ensures
a fast training convergence and fairness among users. Finally,
simulation results are provided to show the superiority of the
proposed DL-based schemes over current baselines, in terms of
both the error performance and receiver complexity.
Original languageEnglish
Pages (from-to)2109-2119
JournalIEEE Transactions on Wireless Communications
Issue number3
Publication statusPublished - 1 Mar 2021

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