Ecole d'ingénieur et centre de recherche en Sciences du numérique

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

Lee, Woongsup; Kim, Minhoe; Cho, Dong-Ho

IEEE Systems Journal, September 2018

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.

Doi Bibtex

Titre:Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication
Département:Systèmes de Communication
Eurecom ref:5692
Copyright: © 2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Bibtex: @article{EURECOM+5692, doi = {}, year = {2018}, month = {09}, title = {{D}eep {L}earning {B}ased {T}ransmit {P}ower {C}ontrol in {U}nderlaid {D}evice-to-{D}evice {C}ommunication}, author = {{L}ee, {W}oongsup and {K}im, {M}inhoe and {C}ho, {D}ong-{H}o}, journal = {{IEEE} {S}ystems {J}ournal, {S}eptember 2018}, url = {} }
Voir aussi: