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Centralized and Distributed Power Allocation for Max-Min Fairness in Cell-Free Massive MIMO
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5954-434X
Univ Pisa, Italy.
2019 (English)In: CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, IEEE , 2019, p. 576-580Conference paper, Published paper (Refereed)
Abstract [en]

Cell-free Massive MIMO systems consist of a large number of geographically distributed access points (APs) that serve users by coherent joint transmission. Downlink power allocation is important in these systems, to determine which APs should transmit to which users and with what power. If the system is implemented correctly, it can deliver a more uniform user performance than conventional cellular networks. To this end, previous works have shown how to perform system-wide max-min fairness power allocation when using maximum ratio precoding. In this paper, we first generalize this method to arbitrary precoding, and then train a neural network to perform approximately the same power allocation but with reduced computational complexity. Finally, we train one neural network per AP to mimic system-wide max-min fairness power allocation, but using only local information. By learning the structure of the local propagation environment, this method outperforms the state-of-the-art distributed power allocation method from the Cell-free Massive MIMO literature.

Place, publisher, year, edition, pages
IEEE , 2019. p. 576-580
Series
Conference Record of the Asilomar Conference on Signals Systems and Computers, ISSN 1058-6393
Keywords [en]
Cell-free Massive MIMO; Power allocation; Max-min fairness; Deep learning; Scalability
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-168230DOI: 10.1109/IEEECONF44664.2019.9048903ISI: 000544249200112ISBN: 978-1-7281-4300-2 (electronic)OAI: oai:DiVA.org:liu-168230DiVA, id: diva2:1459394
Conference
53rd Asilomar Conference on Signals, Systems, and Computers
Note

Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Available from: 2020-08-19 Created: 2020-08-19 Last updated: 2020-08-19

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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