## 1 Introduction
Hence, back in Figure 1, the optimal gain can be maintained only at \(t_{p1}\), whereas at \(t_{1}\) and \(t_{2}\), as the angle error increases, the gain decreases. Based on the above analysis, to maintain optimum and stable gains, the problem of the target UAV's high mobility must be addressed. In [14; 19], DoA estimation methods are proposed to exploit multi-path signals. To further distinguish the LoS signals from the multi-path components, an estimation method is proposed in [20], which explores the strongest line-of-sight channel. The algorithms in [14; 16; 19; 20] adopt the same eigen-decomposition method, which is applied in the traditional Multiple Signal Classification (MUSIC) algorithms. The computational complexity of these algorithms is generally high and their accuracy can be severely affected when the noise is intense. Therefore, in [17; 21], DoA estimation methods of lower complexity and higher accuracy have been proposed. The methods introduced above cannot maintain highly accurate estimations for fast-moving UAVs without incurring a high system training overhead, because they focus on a single time's connection via beamforming, without giving enough attention to the realistic mechanism of the cellular system. In these schemes, the typical solution to the UAV's high mobility problem is to shorten the pilot signal interval (\(t_{p2}-t_{p1}\)), so that the DoA information is updated frequently to avoid large decrease in gain. However, this solution also requires a higher number of pilot signals to be transmitted for each user in the network. The work in [14, 20] have reduced the required number of pilot signals in each beamforming training, however, these methods still heavily rely on training sequences because each DoA estimation at least requires one pilot signal. In future cellular networks, the number of users is expected to be increased by a factor on the order of tens [22], and if each individual user requires a large number of pilot signals, the total training overhead will be greatly increased and occupy an excessive amount of the bandwidth originally allocated for data transmission. Furthermore,  
Figure 1: The proposed beamforming scheme versus conventional beamforming scheme for the highly-mobile UAV communication scenario. The proposed scheme updates DoA information in real-time to maintain highly stable gains in uplink connections. Figure 2: Illustration of the DoA estimation error caused by UAV’s mobility during pilot signal intervals. most real-world deployment of the cellular system will set a minimum interval for transmitting pilot signals, which restricts the frequency for DoA estimation. In [23; 24], position-aided beamforming methods are proposed for high-speed train communications. Since the track of the train is known to the BS processor, beamforming can be optimised by casting the highest gain to the train's antenna at any moment, without the assistance of pilot signals. However, these methods are not applicable in UAV communications, since most UAVs do not have pre-defined trajectories. Even if trajectories of UAVs are known in some application fields, such tracks are far less precise than the ground rail way case. In this paper, we propose an algorithm for evaluating the DoA of a UAV by utilizing position information obtained from the UAV's flight status information (FSI). The algorithm incorporates a prediction method for tracking the angle variations during intervals between the reception of FSI update messages. Since the prediction process does not require the transmission of pilot signals, it can be executed frequently without increasing the training overhead. In this way, a steady and accurate output of DoA estimations is obtained. The proposed algorithm enables the realization of a novel beamforming scheme. As illustrated in the lower half of Figure 1, instead of using the DoA calculated at \(t_{p1}\), this scheme allows the weight vector to be obtained using up-to-date DoA values generated via prediction at times between \(t_{p1}\) and \(t_{p2}\).