Graduate School and Research Center in Digital Sciences

Deep power control: Transmit power control scheme based on convolutional neural network

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

IEEE Communications Letters, April 2018, ISSN: 1089-7798

In this paper, deep power control (DPC), which is the first transmit power control framework based on a convolutional neural network (CNN), is proposed. In DPC, the transmit power control strategy to maximize either spectral efficiency (SE) or energy efficiency (EE) is learned by means of a CNN. While conventional power control schemes require a considerable number of computations, in DPC the transmit power of users can be determined using far fewer computations enabling real-time processing.We also propose a form of DPC that can be performed in a distributed manner with local channel state information (CSI), allowing the signaling overhead to be greatly reduced. Through simulations, we show that the DPC can achieve almost the same or even higher SE and EE than a conventional power control scheme, with a much lower computation time.

Doi Bibtex

Title:Deep power control: Transmit power control scheme based on convolutional neural network
Department:Communication systems
Eurecom ref:5529
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Bibtex: @article{EURECOM+5529, doi = {}, year = {2018}, month = {04}, title = {{D}eep power control: {T}ransmit power control scheme based on convolutional neural network}, author = {{L}ee, {W}oongsup and {K}im, {M}inhoe and {C}ho, {D}ong-{H}o}, journal = {{IEEE} {C}ommunications {L}etters, {A}pril 2018, {ISSN}: 1089-7798}, url = {} }
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