Flight Sensor Data and Beamforming based Integrated UAV Tracking with Channel Estimation using Gaussian Process Regression

With explosively increasing demands for unmanned aerial vehicle (UAV) applications, reliable link acquisition for serving UAVs is required. Considering the dynamic characteristics of UAV, it is hugely challenging to persist a reliable link without beam misalignment. In this paper, we propose a flight sensor data and beamforming signal based integrated UAV tracking scheme to deal with this problem. The proposed scheme provides a compatible integrated system considering the practical specification of the flight sensor data and the beamforming pilot signal. The UAV position tracking is comprised of two steps: 1) UAV position prediction by the flight sensor data and 2) position update with the beamforming signal using Gaussian process regression (GPR) method, which is a nonparametric machine learning. The flight sensor data can assist ground station (GS) or UAV nodes in designing the precoding and the receive beamforming matrix with drastically reduced overheads. The beamforming signal can accomplish high beamforming gain to be maintained even when the flight sensor data is absent. Therefore, the proposed scheme can support the moving target continuously by utilizing these two signals. The simulation results are provided to confirm that the proposed scheme outperforms other conventional beam tracking schemes. We also derive 3-dimensional (3D) beamforming gain and spectral efficiency (SE) from the mean absolute error (MAE) of the angular value estimation, which can be used as beamforming performance metrics of the data transmission link in advance

Millimeter-Wave Full-Duplex UAV Relay: Joint Positioning, Beamforming, and Power Control

In this paper, a full-duplex unmanned aerial vehicle (FD-UAV) relay is employed to increase the communication capacity of millimeter-wave (mmWave) networks. Large antenna arrays are equipped at the source node (SN), destination node (DN), and FD-UAV relay to overcome the high path loss of mmWave channels and to help mitigate the self-interference at the FD-UAV relay. Specifically, we formulate a problem for maximization of the achievable rate from the SN to the DN, where the UAV position, analog beamforming, and power control are jointly optimized. Since the problem is highly non-convex and involves high-dimensional, highly coupled variable vectors, we first obtain the conditional optimal position of the FD-UAV relay for maximization of an approximate upper bound on the achievable rate in closed form, under the assumption of a line-of-sight (LoS) environment and ideal beamforming. Then, the UAV is deployed to the position which is closest to the conditional optimal position and yields LoS paths for both air-to-ground links. Subsequently, we propose an alternating interference suppression (AIS) algorithm for the joint design of the beamforming vectors and the power control variables. In each iteration, the beamforming vectors are optimized for maximization of the beamforming gains of the target signals and the successive reduction of the interference, where the optimal power control variables are obtained in closed form. Our simulation results confirm the superiority of the proposed positioning, beamforming, and power control method compared to three benchmark schemes. Furthermore, our results show that the proposed solution closely approaches a performance upper bound for mmWave FD-UAV systems.Comment: This paper has been accepted by IEEE Journal on Selected Areas in Communications, special issue on Multiple Antenna Technologies for Beyond 5