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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applying it to extensive channel measurements and comparing the PA predictions to Kalman-based channel predictions and outdated channel estimates.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.

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 present 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 top of 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 Squared 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.

Connected vehicles in large numbers will be expensive in terms of power and bandwidth unless advanced transmit schemes are employed. These would rely on channel state information at transmitter (CSIT), which rapidly becomes outdated for fading vehicular channels. We here evaluate the predictor antenna concept, that solves this problem by using antennas on the outside of vehicles, with one extra antenna in front of the others. Its estimated channel is a scaled prediction for the channels encountered by rearward antennas when they reach that position. We evaluate this concept on a large set of channel sounding measurements from an urban environment. Recent investigations of the correlations of these measurements indicate that the average normalized mean squared errors (NMSEs) of the complex valued channel predictions should be around -10 dB for prediction horizons in space of up to 3 wavelengths. This represents an extension of the attainable prediction horizon by an order of magnitude, as compared to Kalman or Wiener extrapolation of past channel measurements. It represents an accuracy that would enable e.g. accurate massive multiple input multiple output (MIMO) downlink beamforming to vehicles. We here perform predictions on a subset of the measurements with good channel-to-estimation error power ratio (SNR). The approximate true channels are here available and we evaluate the performance on a validation data set. The results confirm that the distribution of the NMSE, over all investigated propagation environments, is close to that obtained by correlation-based models and outperforms the use of outdated channel measurements.

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., This paper has been presented as a poster on: the 8th European Conference on Antennas and Propagation (EuCAP), in The Hague, The Netherlands, on 6-11 April 2014, and will appear in the proceedings

Predictor antennas (PAs) are a potential solution to severe channel aging that can occur at high vehicular velocities in non line-of-sight (NLOS) environments. Channel aging reduces the performance of many advanced communication schemes based on channel state information at the transmitter (CSIT). Although PAs have been shown to work in combination with dense pilots in time and space, prediction performance can be reduced when channel estimates are sparse. This paper answers how densely pilots must be placed for PAs to be feasible when performing basic interpolation between channel estimates. This is important, especially for establishing upper limits on the length of the downlink (DL) frames required in a time-division duplex (TDD) system with PAs. First, nearest-neighbor, linear, and spline interpolation are analyzed when applied to stochastic radio channels. A theoretical expression is derived for the power of the expected interpolation error for any interpolation method that can be expressed as a linear function of a set of measured values. The interpolation methods are evaluated on three theoretical channels with Rayleigh, flat, and Rician fading, and on two sets of channel measurements. For a given requirement on interpolation normalized mean-squared error (NMSE), a minimum pilot density is extracted, both for theoretical channels and measured channels. The interpolation techniques are used to evaluate the PA channel prediction for different distances between pilots. The results indicate that linear and spline interpolation can be used with down to five and three samples per wavelength, respectively, without affecting the PA-based prediction NMSE. At two samples per wavelength, the prediction NMSE is still at a level that can be useful for precoding design in massive multiple-input multiple-output (M-MIMO) systems. These results can be used to adapt the uplink/downlink frame rate in TDD to the speed of the terminals, if technically possible.