Predictor Antennas : Enabling channel prediction for fast-moving vehicles in wireless broadband systems

Abstract: Many advanced transmission techniques utilize channel state information (CSI) at the transmitter (CSIT) to improve throughput, spectral efficiency, power efficiency, and other performance metrics. Estimating CSI accurately is important to fully benefit from many of these techniques. In situations where users travel at high speed, the channel can change rapidly, especially in small-scale fading environments. In many systems, there is also a delay between measuring CSI and using it for transmission. If the channel changes significantly during this delay, CSI becomes outdated and the benefits of advanced transmission techniques are typically negatively affected. Long-range channel prediction can be used to counteract this delay and enable advanced transmission to vehicles that travel at high velocity. Conventional prediction methods use channel extrapolation and have a limited prediction horizon that does not support high vehicular velocities for the current size of these delays. The predictor antenna concept has been shown to increase the prediction horizon by at least an order-of-magnitude. It does so by placing an antenna array on the exterior of a vehicle, in the direction of travel. The first antenna can then measure the channel at positions that the following antennas will visit later.This thesis uses channel measurements to investigate how practical aspects affect the prediction performance of predictions based on predictor antennas. It also develops a general framework that can be used to calculate the predictions in a real system. This includes addressing the causality of all the processing methods involved and adapting these methods to the design of the system and the radio environment. In a massive multiple-inputmultiple-output (MIMO) system, multi-user transmission is enabled by channel prediction and increases the sum capacity by 100% compared to 1 ms old channel estimates at a velocity of 150 km/h. This is achieved with relatively dense pilots in time. The prediction performance of the proposed framework is shown to degrade if pilots are spread further than 0.3–0.5 wavelengths in space, if spline interpolation is used to interpolate between the channel estimates.