Decentralized learning based indoor interference mitigation for 5G-and-beyond systems

Wang, Y., Feng, G., Sun, Y. , Qin, S. and Liang, Y.-C. (2020) Decentralized learning based indoor interference mitigation for 5G-and-beyond systems. IEEE Transactions on Vehicular Technology, 69(10), pp. 12124-12135. (doi: 10.1109/TVT.2020.3012311)

222923.pdf - Accepted Version



Due to the explosive growth of data traffic and poor indoor coverage, ultra-dense network (UDN) has been introduced as a fundamental architectural technology for 5G-and-beyond systems. As the telecom operator is shifting to a plug-and-play paradigm in mobile networks, network planning and optimization become difficult and costly, especially in residential small-cell base stations (SBSs) deployment. Under this circumstance, severe inter-cell interference (ICI) becomes inevitable. Therefore, interference mitigation is of vital importance for indoor coverage in mobile communication systems. In this paper, we propose a fully distributed self-learning interference mitigation (SLIM) scheme for autonomous networks under a model-free multi-agent reinforcement learning (MARL) framework. In SLIM, individual SBSs autonomously perceive surrounding interferences and determine downlink transmit power without necessity of signaling interactions between SBSs for mitigating interferences. To tackle the dimensional disaster of joint action in the MARL model, we employ the Mean Field Theory to approximate the action value function to greatly decrease the computational complexity. Simulation results based on 3GPP dual-stripe urban model demonstrate that SLIM outperforms several existing known interference coordination schemes in mitigating interference and reducing power consumption while guaranteeing UEs' quality of service for autonomous UDNs.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Liang, Professor Ying-Chang and Feng, Professor Gang and Sun, Dr Yao
Authors: Wang, Y., Feng, G., Sun, Y., Qin, S., and Liang, Y.-C.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Transactions on Vehicular Technology
ISSN (Online):1939-9359
Published Online:28 July 2020
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in IEEE Transactions on Vehicular Technology 69(10): 12124-12135
Publisher Policy:Reproduced in accordance with the publisher copyright policy

University Staff: Request a correction | Enlighten Editors: Update this record