A Framework for Predictor Antennas in Practice

Channel predictions are important to achieve high spectral efficiency for high-mobility vehicles. Channel extrapolation, used by many prediction methods, suffers from a limited prediction horizon in difficult radio environments. The predictor antenna (PA) concept provides the prediction horizons req...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2022-07, Vol.71 (7), p.7503-7518
Main Authors: Bjorsell, Joachim, Sternad, Mikael, Grieger, Michael
Format: Article
Language:English
Subjects:
Antenna arrays
Antenna measurements
Antennas
Channel estimation
Channel prediction
channel state information (CSI)
Delays
Electrical Engineering with specialization in Signal Processing
Elektroteknik med inriktning mot signalbehandling
Frequency measurement
High-mobility
Predictor antenna
Signal processing
Transmitting antennas
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Summary:Channel predictions are important to achieve high spectral efficiency for high-mobility vehicles. Channel extrapolation, used by many prediction methods, suffers from a limited prediction horizon in difficult radio environments. The predictor antenna (PA) concept provides the prediction horizons required for efficient transmission to fast-moving vehicles by measuring the channel ahead of time with an extra antenna placed on the vehicle. This paper presents a general framework that addresses the practical signal processing challenges of the PA concept. It is adaptable to a vast variety of vehicular deployment, mobility, and communication scenarios. A new theoretical prediction normalized mean-squared error (NMSE) expression is derived based on the presented framework. The framework is demonstrated by applying it to extensive channel measurements and comparing the PA predictions to Kalman-based channel predictions and outdated channel estimates. By studying the impact of vehicular velocity and radio environment on the prediction performance, it is shown that PA prediction is weaker at low velocities, where Kalman prediction methods are sufficient, but is uncontested at high velocities in environments without a dominating path. At high velocities in dominating path environments, the Kalman predictor provides usable predictions, but it is still outperformed by the PA predictions.
ISSN:0018-9545
1939-9359
1939-9359
DOI:10.1109/TVT.2022.3168225