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Unmanned aerial vehicles (UAVs) have attracted considerable attention, thanks to their high flexibility, on-demand deployment and the freedom in trajectory design. The communication channel quality can be effectively improved by using UAV to build a line-of-sight communication link between the transmitter and the receiver. Furthermore, there is increasing demand for communication security improvement, as the openness of a wireless channel brings serious threat. This paper formulates a secrecy capacity optimization problem of a UAV-enabled relay communication system in the presence of malicious eavesdroppers, in which the secrecy capacity is maximized by jointly optimizing the UAV relay’s location, power allocation, and bandwidth allocation under the communication quality and information causality constraints. A successive convex approximation–alternative iterative optimization (SCA-AIO) algorithm is proposed to solve this highly coupled nonconvex problem. Simulation results demonstrate the superiority of the proposed secrecy transmission strategy with optimal trajectory design and resource allocation compared with the benchmark schemes and reveal the impacts of communication resources on system performance.

This paper studies unmanned aerial vehicle (UAV)‐assisted cognitive network, where the UAV can improve the communication quality of edge users. Short packet communication (SPC) is widely used due to its low delay transmission characteristic. Unlike long packet communication in conventional wireless networks, SPC has a non‐negligible packet error rate and its data transmission rate is less than Shannon capacity. Considering the fact that the UAV is usually powered by battery, the energy efficiency (EE) maximisation problem is investigated based on short packet transmission in the UAV‐assisted cognitive network. Firstly, the closed‐form expression of EE is analysed, and then the optimisation problem is formulated by jointly optimising the spectrum sensing time, packet error rate, the flight speed, and the coverage range of UAV. Secondly, the optimisation problem is solved by dividing it into four subproblems. Then, an efficient iterative algorithm is proposed to tackle this problem. Simulation results show that the proposed optimisation scheme can evidently improve the EE performance compared with other benchmark schemes. In addition, the proposed joint optimisation algorithm not only has better convergence than exhaustive method, but also has higher stability than PSO algorithm.

Unmanned aerial vehicles (UAVs) are considered an important component of 6G wireless technology. However, there are many challenges to the employment of UAVs, one of which is spectrum scarcity. To address this challenge, non-orthogonal multiple access (NOMA) and cognitive radio (CR) techniques are employed in UAV short-packet communication systems. In this paper, we consider a NOMA-based cognitive UAV short-packet communication system. Firstly, a mathematical expression for the effective throughput of the secondary users is derived. Then, we aim to maximize the effective throughput of the far secondary user by optimizing the sensing time, power allocation, and information bits under the constraints of the transmission power and effective decoding error probability. A joint optimization algorithm is used to solve this problem, where the bisection method and the one-dimensional linear search algorithm are used to solve the subproblem. The simulation results show that the proposed algorithm has low complexity and similar performance compared to the exhaustive method. In addition, the necessity of joint optimization is shown in the simulation results.

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