Joint Trajectory Optimization and Spectrum Access for Cognitive UAV Networks

Abstract

In this paper, we consider a cognitive unmanned aerial vehicle network (CUAVN), which consists of a leading unmanned aerial vehicle (LUAV) and a group of following unmanned aerial vehicles (FUAVs), and where the FUAVs fly irregularly from a data collection point to the next data collection point and transmit collected data to the LUAV. Our goal is to maximize the achievable throughput of the CUAVN by jointly adapting the FUAVs sensing duration, the trajectory of the FUAVs, and the transmit power of the FUAVs under the constraints of maximum interference, maximum UAV speed, UAV collision avoidance and the total flying periods. In the challenging scenarios, the original non-convex problem is addressed by iteratively optimizing three decoupled subproblems: sensing duration optimization, FUAVs trajectory optimization, and FUAVs power allocation optimization. Since there are highly non-convex objective function and constraints in the FUAV trajectory optimization subproblem, we convert it into a more tractable convex problem by utilizing difference-of-convex (DC) functions. Furthermore, a three dimensional (3D) spatial-temporal sensing scheme based on UAVs is introduced into cognitive UAV systems with multiple primary transmit-receive pairs (PTRs) to further improve spectrum efficiency. Simulation results show that the achievable throughput of the FUAVs with the proposed scheme rises by approximately 53% compared with the FUAV fixed trajectory flight scheme and by approximately 58% in comparison with the only timedimension spectrum sensing scheme.

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

“…• Applying spectrum sensing -as of now, it is uncertain whether spectrum sensing methodologies will be unified in future networks. ED is the most prominent method, especially for 3D spectrum assessment [67], [68], [128], and with the latest developments, it can achieve fair performance even for SNR 0 dB without significant computational complexity. EBD and blind detectors, despite their increased complexity, are also promising for achieving accurate detection in low SNR, with a greater degree of autonomy.…”

Bayesian detection with feedback for cooperative spectrum sensing in cognitive UAV networks

“…In the past, the multi‐UAV CSS framework for efficiently achieving spectrum resources has been extensively studied in cognitive UAV networks, such as References 10–30. In detail, a comprehensive survey is given in Table 1.…”

“…Based on the consideration of flight trajectory and Reference 19, the throughput optimization problem is studied in References 23, 24, and the sensing duration, threshold, transmission power and following UAV trajectory in cognitive UAV networks are jointly optimized. In Reference 25, Y. Gu et al optimized the sensing radian and flight trajectory of UAV from a resource allocation perspective.…”