A Calibrated Learning Approach to Distributed Power Allocation in Small Cell Networks
- Resource Type
- Conference
- Authors
- Zhang, Xinruo; Nakhai, Mohammad Reza; Zheng, Gan; Lambotharan, Sangarapillai; Ottersten, Bjorn
- Source
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2019 - 2019 IEEE International Conference on. :8419-8423 May, 2019
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Signal Processing and Analysis
Signal to noise ratio
Interference
Prediction algorithms
Resource management
Microcell networks
Calibration
Games
small cell networks
distributed power allocation
online learning
calibration
- Language
- ISSN
- 2379-190X
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.