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

Abstract

In this paper, we study a data driven real-time machine learning method for end-to-end wireless systems for Internet of Things (IoT). For a two-way communication link between two IoT devices, we propose an efficient learning algorithm that can train the auto-encoder 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 communications scenarios.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Internet of Things Journal
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Auto-encoder
  • End-to-End design
  • Generators
  • Internet of Things
  • Machine learning
  • Real-time systems
  • Receivers
  • Training
  • Transmitters
  • Wireless communication

ASJC Scopus subject areas

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

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