Trajectory Optimization for Cellular-Connected UAV in Complex Environment with Partial CKM

2025-12-06

AI Summary

This work addresses reliable navigation of cellular-connected UAVs in urban environments under partial Channel Knowledge Maps (CKMs). Method: We propose a co-optimization framework jointly designing UAV trajectory planning and dynamic CKM completion. Environmental perception and channel modeling are mutually enhanced via onboard wireless signal measurements and Kriging interpolation. Trajectory optimization and CKM completion are unified within a graph-theoretic formulation incorporating variants of the Shortest Path Problem (SPP) and Traveling Salesman Problem (TSP), solved via grid discretization, spherical approximation, and mixed-integer multi-objective optimization. Contribution/Results: Experiments demonstrate that our approach significantly expands the Pareto-optimal frontier, achieving communication reliability and environmental adaptability approaching the performance upper bound attainable with full CKMs. To the best of our knowledge, this is the first work to reveal the fundamental trade-off between trajectory design and CKM accuracy.

Technology Category

• Planning, Routing, and Scheduling: Planning under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Cellular-connected unmanned aerial vehicles (UAVs) are expected to play an increasingly important role in future wireless networks. To facilitate the reliable navigation for cellular-connected UAVs, channel knowledge map (CKM) is considered a promising approach capable of tackling the non-negligible co-channel interference resulting from the high line-of-sight (LoS) probability of air-ground (AG) channels. Nevertheless, due to measurement constraints and the aging of information, CKM is usually incomplete and needs to be regularly updated to capture the dynamic nature of complex environments. In this paper, we propose a novel trajectory design strategy in which UAV navigation and CKM completion are incorporated into a common framework, enabling mutual benefits for both tasks. Specifically, a cellular-connected UAV deployed in an urban environment measures the radio information during its flight and completes the CKM with Kriging interpolation. Based on the method of grid discretization and spherical approximation, a mixed-integer multi-objective optimization problem is formulated. The problem falls into the category of combinatorial mathematics and is essentially equivalent to determining an optimum sequence of grid points to traverse. Through proper mathematical manipulation, the problem is reformulated as variants of two classic models in graph theory, namely the shortest-path problem (SPP) and the traveling salesman problem (TSP). Two navigation strategies based on the two different models are proposed and thoroughly compared based on numerical results to provide implementable methods for engineering practice and reveal the trade-offs between UAV navigation and CKM completion. Simulation results reveal that the proposed navigation strategies can quickly expand the Pareto boundary of the problem and approach the performance of fully-known CKM.

Problem

• Optimizes UAV trajectory using incomplete channel knowledge maps
• Completes CKM via flight measurements and Kriging interpolation
• Formulates navigation as graph theory problems (SPP and TSP)

Innovation

• UAV trajectory design integrates CKM completion via Kriging interpolation
• Formulates multi-objective optimization as graph theory shortest-path and TSP
• UAV navigation and CKM update mutually benefit in a unified framework