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Blockage-Aware UAV-Assisted Wireless Data Harvesting With Building Avoidance

Gitae Park, Kanghyun Heo, Kisong Lee

TL;DR

The paper tackles blockage-aware UAV data harvesting in urban environments by jointly optimizing scheduling and 3D UAV trajectories while enforcing building avoidance. It introduces a mathematically rigorous model for cuboid-building blockage and a generalized LoS/NLoS channel-state determination applicable across altitudes, reformulating the problem as a nonconvex $MINLP$. The solution uses decomposition into convex subproblems via quadratic transform ($QT$) and successive convex approximation ($SCA$), with building-avoidance via a separating hyperplane method and an approximated indicator function. An iterative block-coordinate-descent algorithm yields trajectories and schedules that preserve LoS links and significantly improve the minimum uplink throughput compared to baselines. Overall, the framework enables safe, blockage-aware UAV data harvesting in dense urban areas and offers insights for practical deployment.

Abstract

Unmanned aerial vehicles (UAVs) offer dynamic trajectory control, enabling them to avoid obstacles and establish line-of-sight (LoS) wireless channels with ground nodes (GNs), unlike traditional ground-fixed base stations. This study addresses the joint optimization of scheduling and three-dimensional (3D) trajectory planning for UAV-assisted wireless data harvesting. The objective is to maximize the minimum uplink throughput among GNs while accounting for signal blockages and building avoidance. To achieve this, we first present mathematical models designed to avoid cuboid-shaped buildings and to determine wireless signal blockage by buildings through rigorous mathematical proof. The optimization problem is formulated as nonconvex mixed-integer nonlinear programming and solved using advanced techniques. Specifically, the problem is decomposed into convex subproblems via quadratic transform and successive convex approximation. Building avoidance and signal blockage constraints are incorporated using the separating hyperplane method and an approximated indicator function. These subproblems are then iteratively solved using the block coordinate descent algorithm. Simulation results validate the effectiveness of the proposed approach. The UAV dynamically adjusts its trajectory and scheduling policy to maintain LoS channels with GNs, significantly enhancing network throughput compared to existing schemes. Moreover, the trajectory of the UAV adheres to building avoidance constraints for its continuous trajectory, ensuring uninterrupted operation and compliance with safety requirements.

Blockage-Aware UAV-Assisted Wireless Data Harvesting With Building Avoidance

TL;DR

The paper tackles blockage-aware UAV data harvesting in urban environments by jointly optimizing scheduling and 3D UAV trajectories while enforcing building avoidance. It introduces a mathematically rigorous model for cuboid-building blockage and a generalized LoS/NLoS channel-state determination applicable across altitudes, reformulating the problem as a nonconvex . The solution uses decomposition into convex subproblems via quadratic transform () and successive convex approximation (), with building-avoidance via a separating hyperplane method and an approximated indicator function. An iterative block-coordinate-descent algorithm yields trajectories and schedules that preserve LoS links and significantly improve the minimum uplink throughput compared to baselines. Overall, the framework enables safe, blockage-aware UAV data harvesting in dense urban areas and offers insights for practical deployment.

Abstract

Unmanned aerial vehicles (UAVs) offer dynamic trajectory control, enabling them to avoid obstacles and establish line-of-sight (LoS) wireless channels with ground nodes (GNs), unlike traditional ground-fixed base stations. This study addresses the joint optimization of scheduling and three-dimensional (3D) trajectory planning for UAV-assisted wireless data harvesting. The objective is to maximize the minimum uplink throughput among GNs while accounting for signal blockages and building avoidance. To achieve this, we first present mathematical models designed to avoid cuboid-shaped buildings and to determine wireless signal blockage by buildings through rigorous mathematical proof. The optimization problem is formulated as nonconvex mixed-integer nonlinear programming and solved using advanced techniques. Specifically, the problem is decomposed into convex subproblems via quadratic transform and successive convex approximation. Building avoidance and signal blockage constraints are incorporated using the separating hyperplane method and an approximated indicator function. These subproblems are then iteratively solved using the block coordinate descent algorithm. Simulation results validate the effectiveness of the proposed approach. The UAV dynamically adjusts its trajectory and scheduling policy to maintain LoS channels with GNs, significantly enhancing network throughput compared to existing schemes. Moreover, the trajectory of the UAV adheres to building avoidance constraints for its continuous trajectory, ensuring uninterrupted operation and compliance with safety requirements.
Paper Structure (13 sections, 2 theorems, 41 equations, 6 figures, 1 table, 1 algorithm)

This paper contains 13 sections, 2 theorems, 41 equations, 6 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Channel State Determination and Problem Reformulation
  4. Proposed Algorithm
  5. Scheduling Optimization
  6. 3D Trajectory and LoS indicator Optimization
  7. Constraint on Channel State Determination \ref{['dsm0']}
  8. Constraint on Building Avoidance \ref{['nffz']}
  9. Constraint on Minimum Spectral Efficiency \ref{['constrk2']}
  10. Problem Transformation
  11. Procedure of Proposed Algorithm
  12. Simulation Results and Discussions
  13. Conclusion

Key Result

Theorem 1

Consider a cuboid $B_{\textrm{exp}}$ with half-width $\frac{\mathcal{W}_B}{2}+\frac{d_{\textrm{max}}}{2\sqrt{2}}$, half-length $\frac{\mathcal{L}_B}{2}+\frac{d_{\textrm{max}}}{2\sqrt{2}}$, and half-height $\frac{\mathcal{H}_B}{2}+\frac{d_{\textrm{max}}}{2\sqrt{2}}$. Suppose that any line segment con

Figures (6)

  • Figure 1: System model of a UAV-assisted wireless communication network.
  • Figure 2: Channel state determination constraints.
  • Figure 3: Convergence behavior of the proposed scheme.
  • Figure 4: Scheduling and trajectory for the proposed scheme.
  • Figure 5: Impact of the proposed building avoidance and channel state determination.
  • ...and 1 more figures

Theorems & Definitions (2)

  • Theorem 1
  • Proposition 1