Real-Time Machine Learning Methods for Two-Way End-to-End Wireless Communication Systems

Seunghwan Baek, Jihwan Moon, Junhee Park, Changick Song, Inkyu Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In this article, we study a data-driven real-time machine learning method for end-to-end wireless systems for the Internet of Things (IoT). For a two-way communication link between two IoT devices, we propose an efficient learning algorithm that can train the autoencoder-based transmitter and receiver in each device without needing to know the channel between two devices. To this end, we adopt the conditional generative adversarial network (cGAN) that can learn an output distribution of the channel for a given conditioning signal. Our proposed training method consists of the link update stage and the self-update stage. In the link update stage, two devices transmit the training data and update their own receiver and the cGAN simultaneously. Subsequently, in the self-update stage, the two devices train their transmitters at the same time for the given receivers and cGANs. Our proposed real-time training method is updated without the knowledge of the channel models nor information feedback for training. Finally, we demonstrate that the proposed training method achieves significant performance gains over conventional schemes in various practical communication scenarios.

Original languageEnglish
Pages (from-to)22983-22992
Number of pages10
JournalIEEE Internet of Things Journal
Issue number22
Publication statusPublished - 2022 Nov 15

Bibliographical note

Publisher Copyright:
© 2014 IEEE.


  • Autoencoder (AE)
  • end-to-end design
  • machine learning (ML)

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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