Applied Sciences (Jun 2020)
DoA Prediction Based Beamforming with Low Training Overhead for Highly-Mobile UAV Communication with Cellular Networks
Abstract
In supporting communications with unmanned aerial vehicles (UAVs) as aerial user equipments (aUEs) in cellular systems, the current beamforming schemes based on channel state estimation are facing severe challenges from the pilot contamination effect, especially in 5G and future networks where the cell size becomes small and the user density is high. Beamforming schemes based on signal direction of arrival (DoA) are regarded as a highly promising alternative to solve this problem. However, to achieve optimal performance for DoA-based beamforming, the error to DoA estimation during pilot signal intervals, caused by the high mobility of UAVs, must be addressed. In the meantime, the training overheads of traditional DoA estimation algorithms must be reduced to save the bandwidth for data communication. This paper investigates uplink beamforming performance enhancement based on signal DoA estimation to support UAV-cellular network communication. We propose a novel DoA estimation algorithm to predict angle variations during the intervals, which achieves high precision even when UAVs are at high mobility. The prediction process requires no pilot signals and enables timely adjustment of the steering vector when calculating the beamforming weight vector. The proposed algorithm contributes to the realisation of a beamforming scheme with real-time steering vector updates, which simultaneously maintains high beamforming gains and low training overheads. Simulation results show that, compared with the conventional DoA-based beamforming scheme, the proposed method yields more accurate DoA estimation output and higher gains. Furthermore, simulation experiments also suggests that applying the proposed scheme can reduce up to 100 pilot signal transmissions per second.
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