Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Handover-Aware URLLC UAV Trajectory Planning: A Continuous-Time Trajectory Optimization via Graphs of Convex Sets

Yuqi Ping, Tingting Zhang, Tianhao Liang

TL;DR

This work tackles handover-aware UAV trajectory planning under finite-blocklength URLLC constraints by jointly optimizing continuous trajectories and BS association. It introduces a Graphs of Convex Sets formulation with Bézier-parameterized trajectory segments and time scaling, translating URLLC requirements into convex feasible regions and encoding connectivity via a directed intersection graph. The optimization becomes a mixed-integer convex program that is relaxed and rounded to yield near-globally optimal, dynamically feasible paths, followed by a refinement step for smoothness. Numerical results demonstrate continuous URLLC connectivity, reduced handovers, and efficient computation, with tunable weights controlling time-optimality under handover penalties.

Abstract

In this paper, we study a cellular-connected unmanned aerial vehicle (UAV) which aims to fly between two predetermined locations while maintaining ultra-reliable low-latency communications (URLLC) for command-and-control (C2) links with terrestrial base stations (BSs). Long-range flights often trigger frequent inter-cell handovers, which may introduce delays and synchronization overhead. We jointly optimize the continuous trajectory and BS association to minimize handovers, path length, and flying time, subject to communication reliability and kinematic constraints. To address this problem, we reformulate it as an optimization based on the graph of convex sets (GCS). First, the URLLC requirement is translated into spatially feasible regions in the flight plane for each BS. And an intersection graph is constructed including the start and goal points. Each graph node is associated with a smooth and dynamically feasible trajectory segment. The trajectory is parameterized in space by Bézier curves and in time by a monotonic Bézier scaling, together with convex constraints that ensure continuity and enforce speed bounds. Next, we impose unit-flow constraints to enforce a single path, and by coupling the resulting binary edge-selection variables with the convex constraints, we obtain a mixed-integer convex program (MICP). Applying a convex relaxation and rounding to the mixed-integer convex program produces nearly globally optimal routes, and a final refinement yields smooth, dynamically feasible trajectories. Simulations verify that the method preserves URLLC connectivity while achieving a clear trade-off between fewer handovers and flight efficiency.

Handover-Aware URLLC UAV Trajectory Planning: A Continuous-Time Trajectory Optimization via Graphs of Convex Sets

TL;DR

This work tackles handover-aware UAV trajectory planning under finite-blocklength URLLC constraints by jointly optimizing continuous trajectories and BS association. It introduces a Graphs of Convex Sets formulation with Bézier-parameterized trajectory segments and time scaling, translating URLLC requirements into convex feasible regions and encoding connectivity via a directed intersection graph. The optimization becomes a mixed-integer convex program that is relaxed and rounded to yield near-globally optimal, dynamically feasible paths, followed by a refinement step for smoothness. Numerical results demonstrate continuous URLLC connectivity, reduced handovers, and efficient computation, with tunable weights controlling time-optimality under handover penalties.

Abstract

In this paper, we study a cellular-connected unmanned aerial vehicle (UAV) which aims to fly between two predetermined locations while maintaining ultra-reliable low-latency communications (URLLC) for command-and-control (C2) links with terrestrial base stations (BSs). Long-range flights often trigger frequent inter-cell handovers, which may introduce delays and synchronization overhead. We jointly optimize the continuous trajectory and BS association to minimize handovers, path length, and flying time, subject to communication reliability and kinematic constraints. To address this problem, we reformulate it as an optimization based on the graph of convex sets (GCS). First, the URLLC requirement is translated into spatially feasible regions in the flight plane for each BS. And an intersection graph is constructed including the start and goal points. Each graph node is associated with a smooth and dynamically feasible trajectory segment. The trajectory is parameterized in space by Bézier curves and in time by a monotonic Bézier scaling, together with convex constraints that ensure continuity and enforce speed bounds. Next, we impose unit-flow constraints to enforce a single path, and by coupling the resulting binary edge-selection variables with the convex constraints, we obtain a mixed-integer convex program (MICP). Applying a convex relaxation and rounding to the mixed-integer convex program produces nearly globally optimal routes, and a final refinement yields smooth, dynamically feasible trajectories. Simulations verify that the method preserves URLLC connectivity while achieving a clear trade-off between fewer handovers and flight efficiency.

Paper Structure

This paper contains 14 sections, 34 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model and Problem Description
  3. General Descriptions
  4. UAV Kinematic Model
  5. UAV to BS Communication Model
  6. Short-Packet URLLC Constraint
  7. Problem Description
  8. GCS-Based Reformulation with Bézier-Parameterized Trajectory
  9. Convex Intersection Graph of URLLC-Feasible Regions
  10. Node-Level Trajectory Representation and Convex Kinematic Constraints
  11. Edge Coupling and Handover-Aware Edge Lengths
  12. Flow-Coupled GCS Program, Convex Relaxation, and Path Extraction
  13. Numerical Results
  14. Conclusion

Figures (4)

  • Figure 1: Illustration of the proposed cellular-connected UAV system.
  • Figure 2: Optimized UAV trajectories under three weighting configurations: Efficiency-Priority, Handover-Constrained, and Handover-Only within a 5 km × 5 km cellular layout.
  • Figure 3: Trajectory comparison under different smoothing regularization strengths.
  • Figure 4: Acceleration profiles corresponding to different smoothing regularization strengths.