Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network

By Woongsup Lee, Minhoe Kim, Dong Ho Cho. 2018

268 citations recorded by the source corpus.

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.

Subfields: Electrical and Electronic Engineering

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Citations in the corpus

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, methodology): “A naive distributed scheme has been proposed in [8] where zero input vector is applied to the trained DNN for the unknown information, while its performance is not sufﬁcient in practice.”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, methodology): “Specifically, for power allocation, the aim of authors in [15], [55], [56] is to utility neural networks to approximate high complexity power allocation algorithms, such as the genetic algorithm and WMMSE algorithm.”
Learning to Rest: A Q-Learning Approach to Flying Base Station Trajectory Design with Landing Spots (2018, result): “While the literature on power control is abundant including for decentralized cases [3] [4], and moreover deep learning approaches [5] [6], this research departs in significant ways from past work in that we consider that each decision making agent (i.”
Resource Allocation for Multi-Channel Underlay Cognitive Radio Network Based on Deep Neural Network (2018, background): “Moreover, given that a trained DNN model with moderate number of layers can be used with a low computation time [7], [8], it is well suited to real-time operations.”
Constrained Deep Learning for Wireless Resource Management (2019, methodology): “The DL techniques have been recently applied to tackle (P3) for the special case PA,i = PP,i [4].”
A Deep Learning Framework for Optimization of MISO Downlink Beamforming (2019, methodology): “Such a hybrid learning method of the supervised and unsupervised learning can signiﬁcantly improve the learning performance and also accelerate convergence [29].”
Machine Learning in the Air (2019, methodology): “This corresponds to the best that could be achieved using DNNs, and it has been shown in the literature [54] that this approach outperforms other state-of-the-art schemes.”
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, methodology): “Inspired by these theoretical results, Lee et al. [222] further showed that, by using a CNN model, the required number of weights to achieve the same performance is much smaller than that in an FNN model.”
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis (2020, background): “Most of the existing works adopt MLPs [7], [9], [11], [21] or convolutional neural networks (CNNs) [8], [12].”
Learning to Cooperate in Decentralized Wireless Networks (2018, result): “While the literature on power control is abundant including for decentralized cases [3] [4], and moreover deep learning approaches [5] [6], this research departs in significant ways from past work in that we consider that each decision making agent (i.”