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Performance Analysis of (TDD) Massive MIMO with Kalman Channel Prediction
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: 0000-0002-2093-5466
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: 0000-0002-7599-4367
2017 (English)In: Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on, 2017, p. 3554-3558Conference paper, Published paper (Refereed)
Abstract [en]

In massive MIMO systems, which rely on uplink pilots to estimate the channel, the time interval between pilot transmissions constrains the length of the downlink.  Since switching between up- and downlink takes time, longer downlink blocks increase the effective spectral efficiency.  We investigate the use of low-complexity channel models and Kalman filters for channel prediction, to allow for longer intervals between the pilots.  Specifically, we quantify how often uplink pilots have to be sent when the downlink rate is allowed to degrade by a certain percentage.  To this end, we consider a time-correlated channel aging model, whose spectrum is rectangular, and use autoregressive moving average (ARMA) processes to approximate the time-variations of such channels.  We show that ARMA-based predictors can increase the interval between pilots and the spectral efficiency in channels with high Doppler spreads.  We also show that Kalman prediction is robust to mismatches in the channel statistics.

Place, publisher, year, edition, pages
2017. p. 3554-3558
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keyword [en]
channel aging, channel estimation, channel prediction, Kalman estimation, massive MIMO
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-135788DOI: 10.1109/ICASSP.2017.7952818ISI: 000414286203143ISBN: 978-1-5090-4117-6 (print)OAI: oai:DiVA.org:liu-135788DiVA, id: diva2:1083742
Conference
International Conference on Acoustics, Speech, and Signal Processing, New Orleans, USA 2017
Available from: 2017-03-22 Created: 2017-03-22 Last updated: 2018-01-12

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Kashyap, SalilMollén, ChristopherEmil, BjörnsonLarsson, Erik G.
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