King's College London

Research portal

Calibrated Learning for Online Distributed Power Allocation in Small-Cell Networks

Research output: Contribution to journalArticle

Xinruo Zhang, Mohammad Reza Nakhai, Gan Zheng, Sangarapillai Lambotharan, Bjo ̈rn Ottersten

Original languageEnglish
Pages (from-to)8124-8136
Number of pages13
JournalIEEE Transactions on Communications
Volume67
Issue number11
Early online date30 Aug 2019
DOIs
Publication statusPublished - Nov 2019

Documents

  • Calibrated Learning for Online_ZHANG_Acc21stAug2019Epub30Aug2019_GREEN AAM

    tcom_authors_version.pdf, 1.57 MB, application/pdf

    18/02/2020

    Accepted author manuscript

    2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

King's Authors

Abstract


This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station (SBS) is modelled as an intelligent agent who autonomously decides on its instantaneous transmit power level by predicting the transmitting policies of the other SBSs, namely the opponent SBSs, in the network, in real-time. The decision making process is based jointly on the past observations and the calibrated forecasts of the upcoming power allocation decisions of the opponent SBSs who inflict the dominant interferences on the agent. Furthermore, we integrate the proposed calibrated forecast process with a bandit policy to account for the wireless channel conditions unknown a priori, and develop an autonomous power allocation algorithm that is executable at individual SBSs to enhance the accuracy of the autonomous decision making. We evaluate the performance of the proposed algorithm in cases of maximizing the long-term sum-rate, the overall energy efficiency and the average minimum achievable data rate. Numerical simulation results demonstrate that the proposed design outperforms the benchmark scheme with limited amount of information exchange and rapidly approaches towards the optimal centralized solution for all case studies.

Download statistics

No data available

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454