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

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

doi:10.1109/lcomm.2018.2825444

Cited by 282 publications

(258 citation statements)

References 7 publications

Supporting

Mentioning

241

Contrasting

Unclassified

Order By: Relevance

“…We generate 20000 training samples, i.e., network realizations, to train MLP, PCNet, and DPC as in [1], [2] while the number of training samples used for IGCNet is 2000. The test dataset contains 500 network realizations.…”

Section: ) Mlp [1]: It Leverages Mlp To Learn the Input-output Mappimentioning

confidence: 99%

See 1 more Smart Citation

A Graph Neural Network Approach for Scalable Wireless Power Control

Shen

Shi

Zhang

et al. 2019

2019 IEEE Globecom Workshops (GC Wkshps)

View full text Add to dashboard Cite

Deep neural networks have recently emerged as a disruptive technology to solve NP-hard wireless resource allocation problems in a real-time manner. However, the adopted neural network structures, e.g., multi-layer perceptron (MLP) and convolutional neural network (CNN), are inherited from deep learning for image processing tasks, and thus are not tailored to problems in wireless networks. In particular, the performance of these methods deteriorates dramatically when the wireless network size becomes large. In this paper, we propose to utilize graph neural networks (GNNs) to develop scalable methods for solving the power control problem in K-user interference channels. Specifically, a K-user interference channel is first modeled as a complete graph, where the quantitative information of wireless channels is incorporated as the features of the graph. We then propose an interference graph convolutional neural network (IGCNet) to learn the optimal power control in an unsupervised manner. It is shown that one-layer IGCNet is a universal approximator to continuous set functions, which well matches the permutation invariance property of interference channels and it is robust to imperfect channel state information (CSI). Extensive simulations will show that the proposed IGCNet outperforms existing methods and achieves significant speedup over the classic algorithm for power control, namely, WMMSE.

show abstract

Section: ) Mlp [1]: It Leverages Mlp To Learn the Input-output Mappimentioning

confidence: 99%

“…We follow [4] to set up the simulation. The link distance is uniformly distributed in [2,10] meters during training. In the test, the link distance is uniformly distributed in [l r , u r ] meters, where l r is uniform in [2,20] meters and u r is uniform in [l r , 20] meters.…”

Section: ) Varying User Locationsmentioning

confidence: 99%

A Graph Neural Network Approach for Scalable Wireless Power Control

Shen

Shi

Zhang

et al. 2019

2019 IEEE Globecom Workshops (GC Wkshps)

View full text Add to dashboard Cite

show abstract

“…Scalability is attained in the processing of signals in time and space with convolutional neural networks (CNNs). Recognizing this fact has led to proposals that adapt CNNs to wireless resource allocation problems [13], [14], [17]. A particularly enticing alternative is the use of a spatial CNN that exploits the spatial geometry of wireless networks to attain scalability to large scale systems with hundreds of nodes [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Eisen

Ribeiro

2020

IEEE Trans. Signal Process.

248

205

View full text Add to dashboard Cite

We consider the problem of optimally allocating resources across a set of transmitters and receivers in a wireless network. The resulting optimization problem takes the form of constrained statistical learning, in which solutions can be found in a model-free manner by parameterizing the resource allocation policy. Convolutional neural networks architectures are an attractive option for parameterization, as their dimensionality is small and does not scale with network size. We introduce the random edge graph neural network (REGNN), which performs convolutions over random graphs formed by the fading interference patterns in the wireless network. The REGNN-based allocation policies are shown to retain an important permutation equivariance property that makes them amenable to transference to different networks. We further present an unsupervised model-free primal-dual learning algorithm to train the weights of the REGNN. Through numerical simulations, we demonstrate the strong performance REGNNs obtain relative to heuristic benchmarks and their transference capabilities.

show abstract

“…Such hybrid learning is a promising technique to achieve both great performance and accelerate the convergence rate of unsupervised learning. In the first stage, supervised learning is used for pre-training and then unsupervised learning will be used for further improvement in the second stage [10]. Getting the best of both learning methods, hybrid learning is an attractive approach known to achieve performance that is better than most existing heuristics.…”

Section: Hybrid Learning For Bnnmentioning

confidence: 99%

Model-Driven Beamforming Neural Networks

Xia

Zheng

Wong

et al. 2020

IEEE Wireless Commun.

View full text Add to dashboard Cite

Beamforming is evidently a core technology in recent generations of mobile communication networks. Nevertheless, an iterative process is typically required to optimize the parameters, making it ill-placed for real-time implementation due to high complexity and computational delay. Heuristic solutions such as zero-forcing (ZF) are simpler but at the expense of performance loss. Alternatively, deep learning (DL) is well understood to be a generalizing technique that can deliver promising results for a wide range of applications at much lower complexity if it is sufficiently trained. As a consequence, DL may present itself as an attractive solution to beamforming. To exploit DL, this article introduces general data-and model-driven beamforming neural networks (BNNs), presents various possible learning strategies, and also discusses complexity reduction for the DL-based BNNs. We also offer enhancement methods such as training-set augmentation and transfer learning in order to improve the generality of BNNs, accompanied by computer simulation results and testbed results showing the performance of such BNN solutions.

show abstract

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

Cited by 282 publications

References 7 publications

A Graph Neural Network Approach for Scalable Wireless Power Control

A Graph Neural Network Approach for Scalable Wireless Power Control

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

Model-Driven Beamforming Neural Networks

Contact Info

Product

Resources

About