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Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning
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
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7599-4367
2019 (English)In: ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), IEEE Communications Society, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates the joint data and pilot power optimization for maximum sum spectral efficiency (SE) in multi-cell Massive MIMO systems, which is a non-convex problem. We first propose a new optimization algorithm, inspired by the weighted minimum mean square error (MMSE) approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime, making it suitable for online optimization in real multi-cell Massive MIMO systems. The numerical result demonstrates that the solution obtained by the neural network is 1% less than the stationary point for four-cell systems, while the sum SE loss is 2% in a nine-cell system.

Place, publisher, year, edition, pages
IEEE Communications Society, 2019.
Series
IEEE International Conference on Communications (ICC), ISSN 1550-3607, E-ISSN 1938-1883
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-156972DOI: 10.1109/ICC.2019.8761234ISI: 000492038801037ISBN: 978-1-5386-8088-9 (electronic)ISBN: 978-1-5386-8089-6 (print)OAI: oai:DiVA.org:liu-156972DiVA, id: diva2:1316477
Conference
IEEE International Conference on Communications (ICC)
Projects
5GWirelessCENIIT
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Note

Funding agencies: European UnionEuropean Union (EU) [641985]; ELLIIT; CENIIT

Available from: 2019-05-18 Created: 2019-05-18 Last updated: 2019-11-11

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Van Chien, TrinhBjörnson, EmilLarsson, Erik G.

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