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arxiveess.SY2026-07-04

Handover-Aware Trajectory Planning for Cellular-Connected UAVs under STL Specifications and URLLC Constraints

Yuqi Ping, Tianhao Liang, Bofeng Zheng, Bingyan Xie, Tingting Zhang

This paper investigates handover-aware trajectory planning for cellular-connected UAVs executing mission-centric tasks under ultra-reliable low-latency communication (URLLC) constraints. Signal temporal logic (STL) provides a formal specification layer for translating mission semantics into time-bounded trajectory requirements, while finite-blocklength URLLC feasibility characterizes reliable command-and-control (C2) links with serving base stations (BSs). We formulate a joint planning problem that optimizes the UAV trajectory, STL mission satisfaction, serving-BS association, and handover behavior. To solve this mixed discrete-continuous problem, we adopt and integrate a Logic Network Flow (LNF) based STL reformulation with Bézier-parameterized motion, disk-shaped URLLC service regions, and binary association variables, so that the resulting mixed-integer quadratically constrained formulation can be solved by standard branch-and-bound solvers. Numerical simulations over a library of STL missions show that the proposed planner can execute different mission specifications under the same cellular map while maintaining URLLC feasibility. The results further reveal how mission timing, handover-aware association, and finite-blocklength stringency jointly affect trajectory shape, serving margin, and computational complexity.

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arxiveess.SY2026-07-24

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arxivcs.ROeess.SY2026-07-24

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arxiveess.SY2026-07-24

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arxiveess.SY2026-07-24

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Zhe Bai, Stephen Harris

This paper introduces a novel multivariate Transformer \emph{StateFormer} that forecasts degradation dynamics of large-scale battery systems. The model learns across time scales, from short-term thermal fluctuations to long-term aging trajectories, enabling accurate prediction of…

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