Rikke Apelfröjd

PhD Thesis, Uppsala University, ISBN 978-91-513-0263-8,
Digital Comprehensive Summaries of Uppsala Dissertations from the Factulty of Science and Technology, April 2018, 116 pp.

Dissertation in Electrical Engineering with specialization in Signal Processing, publicly examined in Häggsalen, Ångström Laboratory, Uppsala on Friday April 27, 2018 at 10.00.

Thesis Opponent: Docent Emil Björnson, Linköping University, Linköping, Sweden.

Comprehensive Summary (116 pages) available in Pdf.

Comprehensive Summary in DIVA database

Paper copies of the complete thesis, including papers, can be obtained from Signals and Systems Group, Uppsala University, Box 534, SE-75121 Uppsala, Sweden.

Abstract:

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 nonorthogonal 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.

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.

Included Papers:

• Paper I: Kalman predictions for multipoint OFDM downlink channels.
• Paper II: Design and measurement based evaluations of coherent JT CoMP: A study of precoding, user grouping and resource allocation using predicted CSI.
• Paper III: Robust linear precoder for coordinated multipoint joint transmission under limited backhaul with imperfect CSI.
• Paper IV: Joint reference signal design and Kalman/Wiener channel estimation for FDD massive MIMO.
  Corresponding Paper in IEEE Trans. on Communications, 2019.
• Paper V: Kalman smoothing for irregular pilot patterns; A case study for predictor antennas in TDD systems.

References on prediction of mobile radio channels:

Licentiate thesis by Rikke Apelfröjd, May 2014 on robust coordinated multipoint transmission based on channel prediction.

PhD Thesis on channel prediction by Torbjörn Ekman, 2002.

PhD Thesis on MIMO OFDM channel prediction by Daniel Aronsson, 2011.

The role of small cells, coordinated multipoint and massive MIMO in 5G, IEEE Communications Magazine, 2014

Proposal of predictor antennas , at IEEE WCNC 2012, with preliminary measurements.

Predictor antennas with compensation of mutual antenna coupling , EuCAP 2014.

Extensive measurements of long-range prediction with prediction antennas , IEEE ICC 2017.

Performance evaluation of coordinated multi-point transmission schemes with predicted CSI, at IEEE PIMRC 2012.

IEEE PIMRC 2007 paper on Kalman predictor design for frequency-adaptive scheduling of FDD OFDMA uplinks.

EUSIPCO 2007 paper on OFDMA uplink channel prediction.

IEEE ICASSP 2005 paper on channel estimation and prediction for adaptive OFDMA/TDMA uplinks based on overlapping pilots.

IST-Summit-2005 paper on adaptive TDMA/OFDMA for wide-area coverage and vehicular velocities, for short assumed latencies.

VTC 2003-paper on Channel estimation and prediction for adaptive OFDM downlinks.

IEEE TCOM 2004 on adaptive modulation for predicted wireless channels

VTC 2002-Fall Conference Paper on unbiased power prediction of broadband radio channels.

VTC 1999Fall Conference Paper on linear and quadratic predictors.