Radio Map-Enabled 3D Trajectory and Communication Optimization for Low-Altitude Air-Ground Cooperation

2025-05-11

AI Summary

This work addresses the joint optimization of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in low-altitude air-ground cooperative systems, aiming to maximize the minimum aggregate uplink data rate of UGVs under quality-of-service (QoS) and navigation constraints. We propose a unified three-dimensional trajectory–power–scheduling co-optimization framework. Its hierarchical solution architecture innovatively integrates: (i) an SCA-based penalized scheduling algorithm, (ii) an SCA-based power control scheme, and (iii) a warm-started particle swarm optimization with crossover and mutation (WS-PSO-CM). Population initialization leverages statistical channel models and 3D radio frequency maps to enable dynamic obstacle avoidance and proximity-aware coordination, thereby mitigating path loss. Experimental results demonstrate that the proposed method improves the minimum aggregate uplink rate by 45.8% and reduces equivalent computational time by 46.7%, significantly enhancing both robustness and real-time performance of low-altitude communications.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Low-altitude economy includes the application of unmanned aerial vehicles (UAVs) serving ground robots. This paper investigates the 3-dimensional (3D) trajectory and communication optimization for low-altitude air-ground cooperation systems, where mobile unmanned ground vehicles (UGVs) upload data to UAVs. We propose a joint optimization algorithm to maximize the minimal sum-rate of UGVs while ensuring quality of service and navigation constraints. The proposed algorithm integrates a successive convex approximation (SCA)-penalty method for UGV-UAV scheduling, an SCA-based approach for UGV transmit power control, and a novel warm-start particle swarm optimization with cross mutation (WS-PSO-CM). The WS-PSO-CM leverages convex optimization results from a statistical channel model to initialize particle swarm, significantly improving the performance, compared with celebrated PSO-CM. Simulation results demonstrate that the proposed algorithm achieves a $45.8$% higher minimal sum-rate compared to the baseline PSO-CM under the same iterations. This gain can be translated to reducing computational time by $46.7$% of PSO-CM. Furthermore, our simulation results reveal that UAVs dynamically adjust trajectories to avoid interference by buildings, and maintain proximity to UGVs to mitigate path-loss.

Problem

• Optimize 3D UAV-UGV trajectories for air-ground cooperation
• Maximize minimal sum-rate of UGVs with QoS constraints
• Enhance communication via joint scheduling and power control

Innovation

• SCA-penalty method for UGV-UAV scheduling
• SCA-based UGV transmit power control
• Warm-start PSO-CM for trajectory optimization