Unsupervised learning for cellular power control
This letter applies a feedforward neural network trained in an unsupervised fashion to the problem of optimizing the transmit powers in cellular wireless systems. Both uplink and downlink are considered, with either centralized or distributed power control. Various objectives are entertained, all of...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Universitat Pompeu Fabra |
| Repositorio: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/47548 |
| Acesso em linha: | http://hdl.handle.net/10230/47548 http://dx.doi.org/10.1109/LCOMM.2020.3027994 |
| Access Level: | acceso abierto |
| Palavra-chave: | Machine learning Neural networks Unsupervised learning Power control Cellular systems |
| Resumo: | This letter applies a feedforward neural network trained in an unsupervised fashion to the problem of optimizing the transmit powers in cellular wireless systems. Both uplink and downlink are considered, with either centralized or distributed power control. Various objectives are entertained, all of them such that the problem can be cast in convex form. The performance of the proposed procedure is very satisfactory and, in terms of computational cost, the scalability with the system dimensionality is markedly superior to that of convex solvers. Moreover, the optimization relies on directly measurable channel gains, with no need for user location information. |
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