Reinforcement Learning-based Interference Coordination for Distributed MU-MIMO

Chang Ge, Sijie Xia, Qiang Chen, Fumiyuki Adachi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In our previous studies, we proposed a graph coloring algorithm (GCA) based on heuristics to solve the interference coordination problem for distributed multi-user multi-input multi-output (MU-MIMO). In this paper, along with the recent advances of machine learning, we propose a reinforcement learning (RL) based GCA for cluster-wise distributed MU-MIMO. The computer simulation confirms that our newly proposed RL-GCA can significantly improve the downlink link capacity compared with other non-intelligent GCAs. Also, an interesting conclusion has been obtained in terms of chromatic number (required minimum number of colors). It is shown that the less chromatic number does not necessarily lead to a better interference coordination. Under the propagation environment assumed in this paper, the best chromatic number which maximizes the achievable link capacity is shown to be 4.

Original languageEnglish
Title of host publicationWPMC 2021 - 24th International Symposium on Wireless Personal Multimedia Communications
Subtitle of host publicationPaving the Way for Digital and Wireless Transformation
PublisherIEEE Computer Society
ISBN (Electronic)9781665427609
DOIs
Publication statusPublished - 2021
Event24th International Symposium on Wireless Personal Multimedia Communications, WPMC 2021 - Okayama, Japan
Duration: 2021 Dec 142021 Dec 16

Publication series

NameInternational Symposium on Wireless Personal Multimedia Communications, WPMC
Volume2021-December
ISSN (Print)1347-6890

Conference

Conference24th International Symposium on Wireless Personal Multimedia Communications, WPMC 2021
Country/TerritoryJapan
CityOkayama
Period21/12/1421/12/16

Keywords

  • Distributed MU-MIMO
  • Frequency Allocation
  • Graph Coloring
  • Interference Coordination
  • Machine Learning
  • Reinforcement Learning

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