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

TL;DR

The paper tackles spectrum scarcity in UAV-enabled aerial CRNs by jointly optimizing the UAV's 3D trajectory, transmit power, and user scheduling under propulsion energy limits and probabilistic LoS channels. It introduces a PLoS-based system model, derives a lower bound on the per-slot rate, and solves a non-convex mixed-integer program via block coordinate descent and successive convex approximation, yielding convex subproblems solvable with CVX. Key contributions include energy-aware 3D trajectory design under PLoS, convex reformulations with slack variables, and demonstration that 3D trajectories improve the average rate while respecting interference constraints to the primary user. The results highlight the trade-offs among altitude, LoS probabilities, IT thresholds, and propulsion energy, providing practical guidance for energy-efficient UAV CRN deployments.

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.

Figures (6)

• Figure 1: UAV-enabled CRN consists of an aerial base station (${B}$), multiple cognitive users (${U_r}$), and a primary user (${D}$). The communication between UAV and TNs has corresponding LoS and NLoS probabilities based on their elevation angles.
• Figure 2: The comparison among different schemes.
• Figure 3: The average rate and user scheduling.
• Figure 4: 3D trajectories of $B$ under different schemes and scenarios.
• Figure 5: Simulation results with different schemes.