Unmanned aerial vehicle (UAV)-based interference source localization is increasingly important in dense 5G-Advanced and emerging 6G networks, where unknown interferers degrade communication reliability and bias measurement-driven channel modeling. However, propagation uncertainty caused by multipath, blockage, and non-line-of-sight (NLOS) propagation makes the measurement-to-location relationship highly nonlinear and time-varying, which limits the reliability of conventional localization methods. To address this challenge, we propose a single-UAV localization framework that uses spatial received signal strength indicator (RSSI) measurements collected along an adaptive flight trajectory to localize a dominant interference source. A robust channel-aware localization algorithm estimates the source location together with its associated uncertainty. A Gaussian process (GP) Bayesian optimization method selects waypoints by maximizing the expected reduction in localization uncertainty. By integrating channel-aware localization with uncertainty-aware waypoint selection, the proposed closed-loop received signal strength difference (RSSD) interference localization framework iteratively updates the source estimate and plans subsequent waypoints. At a signal-to-noise ratio (SNR) of 20 dB under multipath propagation, the proposed framework reduces the flight distance for a given localization root mean square error (RMSE) by about 60% and the localization RMSE at a given flight distance by about 70% compared with the baseline methods.
Low-altitude wireless networks have emerged as a promising platform for enabling the safe and efficient operation of unmanned aerial vehicles (UAVs). However, due to the limited spectrum and airspace resources, it is challenging to efficiently accomplish UAV flight tasks without collisions. In this paper, we propose a sequential framework with two coupled stages that coordinates spectrum allocation and airspace planning to construct efficient low-altitude air corridors. Specifically, the low-altitude airspace is discretized into a set of digital grids, where obstacles are modeled as impermeable units. Then, we formulate an optimization problem to minimize the total traversal cost of air corridors, which is challenging to solve due to the tight coupling between spectrum allocation and path planning.Therefore, we first design a constrained Vickrey-Clarke-Groves (VCG) ascending auction mechanism to allocate the spectrum resources. Then, we propose a joint spectrum and airspace resource allocation algorithm to minimize the total traversal cost of air corridors. Finally, simulation results show that the proposed algorithms achieve lower total costs than the baseline algorithms.
Ya-Fei Guo, Ziye Jia, Lei Zhang et al.· 0 citations
With the rapid growth of the low-altitude economy, the number of unmanned aerial vehicles (UAVs) has grown rapidly. It is challenging to plan substantial UAV trajectories in complex urban low-altitude airspace, considering the airspace capacity, inter-vehicle safety, and communication reliability. To deal with this challenge, we propose a dual-scale grid-based trajectory planning approach that separates the global routing and local refinement. Specifically, we discretize the three-dimensional airspace into coarse macro-grids for capacity-constrained routing and high-quality communication-aided fine micro-grids for collision-free trajectory refinement. Both consider an altitude-dependent energy model. To handle the complex dual-scale grid trajectory planning of UAVs, we propose a priority-driven dual-grid Theta* with adaptive relaxation (DGTAR) to balance the global planning efficiency and local obstacle avoidance accuracy. First, we design a priority-driven capacity allocation mechanism to enforce safe separation among UAVs. Then, a combined planner is proposed, which integrates a Theta*-enhanced global search with a sampling-based refinement algorithm, invoking on-demand boundary relaxation to ensure the feasibility. Simulation results reveal that the proposed method DGTAR achieves reductions in many aspects compared with benchmark mechanisms, while maintaining a high planning success rate in congested scenarios.
Xin Zhang, Guang Cheng, Chao Wang et al.· Mathematics· 0 citations
Low-altitude wireless networks have emerged as a promising platform for enabling the safe and efficient operation of unmanned aerial vehicles (UAVs). However, due to the limited spectrum and airspace resources, it is challenging to efficiently accomplish UAV flight tasks without collisions. In this paper, we propose a sequential framework with two coupled stages that coordinates spectrum allocation and airspace planning to construct efficient low-altitude air corridors. Specifically, the low-altitude airspace is discretized into a set of digital grids, where obstacles are modeled as impermeable units. Then, we formulate an optimization problem to minimize the total traversal cost of air corridors, which is challenging to solve due to the tight coupling between spectrum allocation and path planning.Therefore, we first design a constrained Vickrey-Clarke-Groves (VCG) ascending auction mechanism to allocate the spectrum resources. Then, we propose a joint spectrum and airspace resource allocation algorithm to minimize the total traversal cost of air corridors. Finally, simulation results show that the proposed algorithms achieve lower total costs than the baseline algorithms.
Ya-Fei Guo, Ziye Jia, Lei Zhang et al.· 0 citations
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