The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Towards Optimal Power Control via Ensembling Deep Neural Networks
Open accessAbstract
A deep neural network (DNN) based power control method that aims at solving the non-convex optimization problem of maximizing the sum rate of a fading multi-user interference channel is proposed. Towards this end, we first present PCNet, which is a multi-layer fully connected neural network that is specifically designed for the power control problem. A key challenge in training a DNN for the power control problem is the lack of ground truth, i.e., the optimal power allocation is unknown. To address this issue, PCNet leverages the unsupervised learning strategy and directly maximizes the sum rate in the training phase. We then present PCNet+, which enhances the generalization capacity of PCNet by incorporating noise power as an input to the network. Observing that a single PCNet(+) does not universally outperform the existing solutions, we further propose ePCNet(+), a network ensemble with multiple PCNets(+) trained independently. Simulation results show that for the standard symmetric K -user Gaussian interference channel, the proposed methods can outperform state-of-the-art power control solutions under a variety of system configurations. Furthermore, the performance improvement of ePCNet comes with a reduced computational complexity.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
(2010) · Cited by 14,134
(2010) · International Conference on Machine Learning · Cited by 13,995
TensorFlow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.08695 · Cited by 11,822
An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980
Shi, Q., Razaviyayn, M., Luo, Z.-Q., & He, C. (2011) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2011.2147784 · Cited by 2,129
Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems
Ye, H., Li, G. Y., & Juang, B.-H. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2017.2757490 · Cited by 2,000
Fractional Programming for Communication Systems—Part I: Power Control and Beamforming
Shen, K., & Yu, W. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2812733 · Cited by 1,935
Self-citationA simple distributed autonomous power control algorithm and its convergence
Foschini, G. J., & Miljanic, Z. (1993) · IEEE Transactions on Vehicular Technology · doi:10.1109/25.260747 · Cited by 1,723
Cited by
A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Simeone, O. (2018) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2018.2881442 · Cited by 516
Shen, Y., Shi, Y., Zhang, J., & Letaief, K. B. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3036965 · Cited by 367
A Deep Learning Framework for Optimization of MISO Downlink Beamforming
Xia, W., Zheng, G., Zhu, Y., Zhang, J., Wang, J., & Petropulu, A. P. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2960361 · Cited by 345
“For example, the DNN was used to predict transmit power [27, 28].”
MethodOptimal Wireless Resource Allocation With Random Edge Graph Neural Networks
Eisen, M., & Ribeiro, A. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.2988255 · Cited by 327
Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks
Liang, L., Ye, H., Yu, G., & Li, G. Y. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2957798 · Cited by 282
Metadata
- Authors
- Liang, F., Shen, C., Yu, W., & Wu, F.
- Venue
- IEEE Transactions on Communications
- Published
- 2020-03
- Volume
- 68
- Issue
- 3
- Pages
- 1760-1776
- ISSN
- 0090-6778 (ISSN-L)
- 1558-0857
- OpenAlex
- W2992716901
- Cited by
- 268
- References
- 52
- FWCI
- 12.51
- Type
- journal article
- Language
- en
- Open access
- green
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