Demo: A Reinforcement Learning-Based Flexible Duplex Systems for B5G with Sub-6 GHz

Soo Min Kim, Han Cha, Seong Lyun Kim, Chan Byoung Chae

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a reinforcement learning-based flexible duplex system for B5G with Sub-6 GHz. This system combines full-duplex radios and dynamic spectrum access to maximize the spectral efficiency. We verify this methodâ™s feasibility by implementing an FPGA-based real-time testbed. In addition, we compare the proposed algorithm with the result derived from the numerical analysis through system-level evaluations.

Original languageEnglish
Title of host publication2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728151786
DOIs
Publication statusPublished - 2020 Apr
Event2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Seoul, Korea, Republic of
Duration: 2020 May 252020 May 28

Publication series

Name2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Proceedings

Conference

Conference2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020
CountryKorea, Republic of
CitySeoul
Period20/5/2520/5/28

Bibliographical note

Funding Information:
IV. ACKNOWLEDGEMENT This work was supported by IITP grant funded by MSIT, Korea (No.2018-0-00923, 2018-0-00170), and ICT Consilience Creative Program (IITP-2019-2017-0-01015).

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation

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