期刊
IEEE COMMUNICATIONS LETTERS
卷 22, 期 6, 页码 1276-1279出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2018.2825444
关键词
Deep learning; convolutional neural network; transmit power control; spectral efficiency; energy efficiency
资金
- Basic Science Research Program through National Research Foundation of Korea (NRF) - Ministry of Education [NRF 2015R1D1A1A01057529]
In this letter, 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, 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.
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