3D Trajectory Design for Energy-constrained Aerial CRNs Under Probabilistic LoS Channel

TL;DR

This paper proposes a joint optimization framework for 3D UAV trajectory, power control, and user scheduling in energy-constrained aerial cognitive radio networks, leveraging probabilistic LoS channels to maximize system throughput.

Contribution

It introduces a novel joint design approach considering energy constraints and probabilistic LoS channels, with a tractable solution via block coordinate descent and convex approximation.

Findings

Optimized UAV trajectories improve average achievable rate.

The proposed algorithm effectively balances energy consumption and communication performance.

Numerical results confirm the benefits of 3D trajectory design in aerial CRNs.

Abstract

Unmanned aerial vehicles (UAVs) have been attracting significant attention because there is a high probability of line-of-sight links being obtained between them and terrestrial nodes in high-rise urban areas. In this work, we investigate cognitive radio networks (CRNs) by jointly designing three-dimensional (3D) trajectory, the transmit power of the UAV, and user scheduling. Considering the UAV's onboard energy consumption, an optimization problem is formulated in which the average achievable rate of the considered system is maximized by jointly optimizing the UAV's 3D trajectory, transmission power, and user scheduling. Due to the non-convex optimization problem, a lower bound on the average achievable rate is utilized to reduce the complexity of the solution. Subsequently, the original optimization problem is decoupled into four subproblems by using block coordinate descent, and each subproblem is transformed into manageable convex optimization problems by introducing slack variables and successive convex approximation. Numerical results validate the effectiveness of our proposed algorithm and demonstrate that the 3D trajectories of UAVs can enhance the average achievable rate of aerial CRNs.