cellular radio; minimax techniques; time division multiple access
Résumé :
[en] This paper studies the problem of max-min fairness power allocation in distributed small cell networks operated under the same frequency bandwidth. We introduce a calibrated learning enhanced time division multiple access scheme to optimize the transmit power decisions at the small base stations (SBSs) and achieve max-min user fairness in the long run. Provided that the SBSs are autonomous decision makers, the aim of the proposed algorithm is to allow SBSs to gradually improve their forecast of the possible transmit power levels of the other SBSs and react with the best response based on the predicted results at individual time slots. Simulation results validate that in terms of achieving max-min signal-to-interference-plus-noise ratio, the proposed distributed design outperforms two benchmark schemes and achieves a similar performance as compared to the optimal centralized design.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Zhang, Xinruo
Nakhai
ZHENG, Gan ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Lambotharan, Sangarapillai
OTTERSTEN, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
A Calibrated Learning Approach to Distributed Power Allocation in Small Cell Networks
Date de publication/diffusion :
17 avril 2019
Nom de la manifestation :
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Lieu de la manifestation :
Brighton, Royaume-Uni
Date de la manifestation :
12-05-2019 to 17-05-2019
Manifestation à portée :
International
Titre de l'ouvrage principal :
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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