The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Learning Optimal Resource Allocations in Wireless Systems
Open accessAbstract
This paper considers the design of optimal resource allocation policies in wireless communication systems, which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constraint, motivating the development of learning methodologies to attempt their solution. To handle stochastic constraints, training is undertaken in the dual domain. It is shown that this can be done with small loss of optimality when using near-universal learning parameterizations. In particular, since deep neural networks (DNNs) are near universal, their use is advocated and explored. DNNs are trained here with a model-free primal-dual method that simultaneously learns a DNN parameterization of the resource allocation policy and optimizes the primal and dual variables. Numerical simulations demonstrate the strong performance of the proposed approach on a number of common wireless resource allocation problems.
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Cites
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Cited by
She, C., Sun, C., Gu, Z., Li, Y., Yang, C., Poor, H. V., & Vucetic, B. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3053601 · Cited by 405
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
“DL can also facilitate resource management [25, 26], including power allocation [27–31].”
BackgroundOptimal 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
Self-citationDeep-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
Spatial Deep Learning for Wireless Scheduling
Cui, W., Shen, K., & Yu, W. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904352 · Cited by 265
Metadata
- Authors
- Eisen, M., Zhang, C., Chamon, L. F. O., Lee, D. D., & Ribeiro, A.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2019-05-15
- Volume
- 67
- Issue
- 10
- Pages
- 2775-2790
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2883094398
- Cited by
- 253
- References
- 41
- FWCI
- 13.92
- Type
- journal article
- Language
- en
- Open access
- green
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- Eisen, M., Zhang, C., Chamon, L. F. O., Lee, D. D., & Ribeiro, A. (2019). Learning Optimal Resource Allocations in Wireless Systems. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2019.2908906
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