Using Predictor Antennas for the Prediction of Small-scale Fading Provides and Order-of-Magnitude Improvement of Prediction Horizons

Joachim Björsell , Uppsala University,
Mikael Sternad , Uppsala University, and
Michael Grieger , AIRRAYS - Wireless Solutions, Dresden, Germany

IEEE International Conference on Communications, ICC,
Workshop WDN-5G ICC2017, Paris, May 2017.

In Pdf

Also: As Technical Report r161, Signals and Systems, Uppsala University,
Version 2.1, February 2017.
Report, version 2.1, in Pdf

Abstract:

Our aim is to investigate long range predictions (up to several wavelengths) of the small-scale fading of radio channels. The purpose is to enable advanced 5G downlink transmission schemes that require accurate channel state information at transmitters, such as massive MIMO and coherent joint transmission, for vehicular users.

We here presents a proof of concept for the recently introduced predictor antenna scheme which promises a significant increase in prediction horizon compared to conventional techniques. Predictor antennas utilize the exterior of moving vehicles by placing antenna arrays on their roofs. They are used to estimate the fading radio channels that are encountered later by the following antennas.

The level of predictability is determined by the correlation between the channel measured at the predictor antenna and the channel that is later encountered by the following antennas when they move to that position.

That correlation, and the resulting prediction errors, are assessed on a large set of measurement data sampled at vehicular velocities, at a carrier frequency of 2.53 GHz, from a multitude of urban fading environments. These represent a wide variety of propagation environments, including narrow and wide roads, intersections, dense urban environments and residential areas.

Using low-pass filtered predictor antenna measurements, the obtained average prediction Normalized Mean Square Error (NMSE) is -11 dB for prediction horizons of 0.25 wavelengths and -8.5 dB for horizons of 3 wavelengths. This represents an order of magnitude increase of the prediction horizons as compared to time-series prediction that typically, in practice, fails to work for prediction beyond 0.3 wavelengths in space.

As a result, we have a tool that enables advanced 5G transmit schemes for vehicular users and vehicle-to-infrastructure links.

Related publications:

Companion paper at IEEE PIMRC 2017, that describes the actual prediction performance, on a data subset with good SNR.

Paper at IEEE WCNC 2012 Original proposal for using "Predictor antennas" for long-range prediction of fast fading for moving relays.

WSA 2018 paper verifying with measurements that predictor antennas enable precise precoding for massive MIMO antennas in non-line-of sigth.

Conference paper at EUCAP 2014 presenting compensation of antenna coupling.

IEEE Intelligent Transportation Systems Magazine 2015: Making 5G adaptive antennas work for very fast moving vehicles.

Paper at Globecom 2016 5G Workshop on the gain by predictor antennas in terms of spectral efficiency and power efficiency when serving connected vehicles by 5G Massive MIMO antennas.

Channel Estimation and Prediction for 5G Applications.
PhD Thesis by Rikke Apelfröjd, Uppsala University 2018.

Channel Estimation and Prediction for MIMO OFDM Systems.
PhD Thesis by Danel Aronsson, Uppsala University 2011.

Prediction of Mobile Radio Channels.
PhD Thesis by Torbjörn Ekman, Uppsala University 2002.

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