cellular radio; minimax techniques; time division multiple access
Abstract :
[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 :
Computer science
Author, co-author :
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)
External co-authors :
yes
Language :
English
Title :
A Calibrated Learning Approach to Distributed Power Allocation in Small Cell Networks
Publication date :
17 April 2019
Event name :
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Event place :
Brighton, United Kingdom
Event date :
12-05-2019 to 17-05-2019
Audience :
International
Main work title :
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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