Uppsala universitet

Analysis and Measurement of Multiple Antenna Systems for Fading Channel Prediction in Moving Relays.

Nima Jamaly , Chalmers,
Rikke Apelfröjd , Uppsala University,
Ana Belén Martinez , TU Dresden,
Michael Grieger , TU Dresden,
Tommy Svensson , Chalmers,
Mikael Sternad Uppsala University, and
Gerhard Fettweis , TU Dresden.

European Conference on Antennas And Propagation, EuCAP 2014, April 6-11 2014, Hauge, The Netherlands.

The performance of wireless data transmission to mobile vehicles is improved if channel state information is available at the transmitter but movement of vehicles causes outdating of channel estimates.

The concept of a predictor antenna has recently been proposed, where an antenna is placed in front of other antennas on the roof of the vehicle to sense the radio environment in advance. This can comparatively provide an order-of-magnitude improvement in channel prediction performance.

A potential problem with this idea is that closely placed antennas will experience mutual electromagnetic couplings. These may reduce the efficiency of the predictor antenna concept if they are not taken into account. In this paper, we discuss about how to treat the forgoing issue and eventually evaluate a promising candidate on measured channels. We argue that only open-circuit voltage method would be realistic for the present application.

The usefulness of the proposed decoupling method is demonstrated on field measurements obtained in downtown Dresden, Germany.

We also partly address the sensitivity of the open-circuit decoupling method to the accuracy of the utilized network parameters.

Related publications:
Original paper proposing predictor antennas at WCNC Paris 2012, with preliminary measurements.

Licentiate Thesis by Rikke Apelfröjd of which this is the fourth paper.

Report by Rikke Apelfröjd on Kalman prediction for multipoint OFDM downlink channels, May 2014.

Channel Estimation and Prediction for MIMO OFDM Systems: Key design and performance aspects of Kalman-based algorithms.
PhD Thesis by Daniel Aronsson, 2012.


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