Transmit power control using deep neural network for underlay device-to-device communication

Woongsup Lee, Minhoe Kim, Dong Ho Cho

Research output: Contribution to journalArticlepeer-review


A transmit power control strategy using a deep neural network (DNN) is proposed for underlay device-to-device (D2D) communication where D2D user equipment (DUE) shares radio resources with cellular user equipment (CUE). In this scheme, a transmit power control strategy for DUE is found with the aid of a newly proposed DNN structure. Both the spectral efficiency (SE) of the DUE and the amount of interference at the CUE are taken into account, such that the SE of the DUE can be improved while alleviating any deterioration in the cellular transmission. Using simulations, we show that the proposed scheme can achieve a high SE of the DUE while properly regulating the interference caused to the CUE, with a low computation time.

Original languageEnglish
Article number8428396
Pages (from-to)141-144
Number of pages4
JournalIEEE Wireless Communications Letters
Issue number1
Publication statusPublished - 2019 Feb

Bibliographical note

Funding Information:
Manuscript received July 3, 2018; accepted July 31, 2018. Date of publication August 7, 2018; date of current version February 19, 2019. This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant NRF-2018R1D1A1B07040796, and in part by the Rural Development Administration through Cooperative Research Program for Agriculture Science and Technology Development funded by the Ministry of Agriculture, Food, and Rural Affairs under Project PJ01229901201801. The associate editor coordinating the review of this paper and approving it for publication was S. Zhou. (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.

Publisher Copyright:
© 2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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