Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication

Woongsup Lee, Minhoe Kim, Dong Ho Cho

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


In this paper, a means of transmit power control for underlaid device-to-device (D2D) communication is proposed based on deep learning technology. In the proposed scheme, the transmit power of D2D user equipment (DUE) is autonomously learned via a deep neural network such that the weighted sum rate (WSR) of DUEs can be maximized by considering the interference from cellular user equipment. Unlike conventional transmit power control schemes in which complex optimization problems have to be solved in an iterative manner, which possibly requires long computation time, in our proposed scheme the transmit power can be determined with a relatively low computation time. Through simulations, we confirm that the proposed scheme achieves a sufficiently high WSR with a sufficiently low computation time.

Original languageEnglish
Article number8472885
Pages (from-to)2551-2554
Number of pages4
JournalIEEE Systems Journal
Issue number3
Publication statusPublished - 2019 Sept

Bibliographical note

Funding Information:
Manuscript received March 16, 2018; revised June 1, 2018 and August 27, 2018; accepted September 10, 2018. Date of publication September 26, 2018; date of current version August 23, 2019. This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (NRF-2018R1D1A1B07040796) and in part by the Rural Development Administration through Cooperative Research Program for Agriculture Science & Technology Development, funded by the Ministry of Agriculture, Food and Rural Affairs under Project PJ01229901201801. (Corresponding author: Minhoe Kim.) W. Lee is with the Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University, Tongyeong 53064, South Korea (e-mail:,

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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