Joint Optimization of UAV Trajectory Statistical Precoding and User Scheduling

• Main Authors: Xingxuan Zuo, Gangtao Han, Xiaomin Mu
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Unmanned aerial vehicle; interference elimination; multi-user channels; precoding; statistical channel state information; trajectory optimization
• Online Access: https://ieeexplore.ieee.org/document/9066955/

Unmanned aerial vehicles (UAVs) as base stations (BSs) are capable of offering wireless connectivity for users without new terrestrial infrastructures. However, fewer antennas can be placed in the UAV-based BS due to its limited space, which also limits the transmission rate of the UAV-based BS. Millimeter wave (mmWave) bands enable large scale antennas to be packed into very small areas to serve multi-users. However, the existence of the interference is non-negligible in the UAV-based BS with mmWave system. The instantaneous channel state information (CSI), which plays a key role in the interference elimination, is difficult to obtain due to the UAV mobility. Compared to the instantaneous CSI, statistical CSI, such as the channel covariance, can be easily acquired by exploiting the channel statistical reciprocity. In this paper, we propose a novel joint optimization problem of the user scheduling, the statistical precoding, and the UAV trajectory in the UAV-based BS with mmWave system to maximize the sum rate of users. The statistical precoding is utilized to alleviate the multi-users interference. Due to the non-convex objective function and constraints, the optimization problem is decomposed into two subproblems. The goal of the first subproblem is to mitigate multi-users interference using statistical CSI and to select the optimal users, while the goal of the second subproblem is to adjust the UAV trajectory to maximize the sum rate of users via transforming the non-convex subproblem into convex optimization. An iterative algorithm is proposed to optimize two subproblems alternatively. The simulation results demonstrate that the proposed joint optimization algorithm is able to achieve good performance.