Secrecy Outage Performance of Ground-to-Air Communications With Multiple Aerial Eavesdroppers and Its Deep Learning Evaluation
IEEE WIRELESS COMMUNICATIONS LETTERS
Authors: Bao, Tingnan; Zhu, Jun; Yang, Hong-Chuan; Hasna, Mazen O.
Abstract
In this letter, we study the secure information transmission from a ground base station (GBS) to a legitimate unmanned aerial vehicle (UAV) user, in the presence of multiple UAV eavesdroppers. To enhance the secrecy performance, the GBS applies beamforming transmission while enforcing a protection zone around it. Utilizing the general kappa-mu shadowed fading distribution to model the ground-to-air channel, we derive an exact expression of the secrecy outage probability (SOP). To further facilitate performance evaluation, we adopt a data-driven approach and develop a deep learning model that can predict the SOP performance with high accuracy and short computation time. Through selected numerical results, we examine the effect of different system parameters on the SOP performance.
Backhaul-Aware Resource Allocation and Optimum Placement for UAV-Assisted Wireless Communication Network
ELECTRONICS
Authors: Xue, Yishi; Xu, Bo; Xia, Wenchao; Zhang, Jun; Zhu, Hongbo
Abstract
Driven by its agile maneuverability and deployment, the unmanned aerial vehicle (UAV) becomes a potential enabler of the terrestrial networks. In this paper, we consider downlink communications in a UAV-assisted wireless communication network, where a multi-antenna UAV assists the ground base station (GBS) to forward signals to multiple user equipments (UEs). The UAV is associated with the GBS through in-band wireless backhaul, which shares the spectrum resource with the access links between UEs and the UAV. The optimization problem is formulated to maximize the downlink ergodic sum-rate by jointly optimizing UAV placement, spectrum resource allocation and transmit power matrix of the UAV. The deterministic equivalents of UE's achievable rate and backhaul capacity are first derived by utilizing large-dimensional random matrix theory, in which, only the slowly varying large-scale channel state information is required. An approximation problem of the joint optimization problem is then introduced based on the deterministic equivalents. Finally, an algorithm is proposed to obtain the optimal solution of the approximate problem. Simulation results are provided to validate the accuracy of the deterministic equivalents, and the effectiveness of the proposed method.