Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1276-1279 |
| Number of pages | 4 |
| Journal | IEEE Communications Letters |
| Volume | 22 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2018 Jun |
Bibliographical note
Funding Information:Manuscript received March 10, 2018; revised April 6, 2018; accepted April 7, 2018. Date of publication April 11, 2018; date of current version June 8, 2018. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF 2015R1D1A1A01057529). The associate editor coordinating the review of this paper and approving it for publication was C. W. Wen. (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:
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All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Computer Science Applications
- Electrical and Electronic Engineering
