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Joint reference signal design and Kalman/Wiener channel estimation for FDD massive MIMO. Extended Report Version.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
2017 (English)Report (Other academic)
Place, publisher, year, edition, pages
Uppsala: Signals and Systems, Uppsala University , 2017.
Series
Report r1701
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-330705OAI: oai:DiVA.org:uu-330705DiVA, id: diva2:1146557
Available from: 2017-10-03 Created: 2017-10-03 Last updated: 2018-03-07
In thesis
1. Channel Estimation and Prediction for 5G Applications
Open this publication in new window or tab >>Channel Estimation and Prediction for 5G Applications
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Accurate channel state information (CSI) is important for many candidate techniques of future wireless communication systems. However, acquiring CSI can sometimes be difficult, especially if the user equipment is mobile in which case the future channel realisations must be estimated/predicted. In realistic settings the predictability of radio channels is limited due to measurement noise, limited model orders and since the fading statistics must be modelled based on a set of limited and noisy training data.

In this thesis, the limits of predictability for the radio channel are investigated. Results show that the predictability is limited primarily due to limitations in the training data, while the model order provides a second order limitation effect and the measurement noise comes in as a third order effect.

Then, a Kalman-based linear filter is studied for potential 5G technologies:

Coherent coordinated multipoint joint transmission, where channel predictions and the covariance matrix of the prediction error are used to design a robust linear precoder, evaluated in a three base station system. Results show that prediction improves the CSI for the pedestrian users such that system delays of 10 ms are acceptable. The use of the covariance matrix is important for difficult user groups, but of less importance with a simple user grouping system proposed.

Massive multiple-input multiple-output (MIMO) in frequency division duplex (FDD) systems were a reduced, suboptimal, Kalman filter is suggested to estimate channels based on non-orthogonal pilots. By introducing a fixed grid of beams, the system generates sparsity in the channel vectors seen by each user, which then estimates its most relevant channels based on unique pilot codes for each beam. Results show that there is a 5 dB loss compared to orthogonal pilots.

Downlink time division duplex (TDD) channels are estimated based on uplink pilots. By using a predictor antenna, which scouts the channel in advance, the desired downlink channel can be estimated using pilot-based estimates of the channels before and after it (in space). Results indicate that, with the help of Kalman smoothing, predictor antennas can enable accurate CSI for TDD downlinks at vehicular velocities of 80 km/h.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 116
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1642
Keywords
Channel estimation, Channel prediction, Channel smoothing, Linear estimation, Kalman filter, Massive MIMO, Coordinated Multipoint transmission, Robust precoding, Predictor antennas, Limits of predictability, Long range predictions
National Category
Signal Processing Telecommunications
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
urn:nbn:se:uu:diva-344270 (URN)978-91-513-0263-8 (ISBN)
Public defence
2018-04-27, Häggsalen, Å10132, Lägerhyddsvägen 1, Uppsala, 10:00 (English)
Opponent
Supervisors
Available from: 2018-04-05 Created: 2018-03-07 Last updated: 2018-04-24

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