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

TL;DR

This paper develops a joint optimization framework for 3D UAV trajectories and communication strategies in low-altitude air-ground systems, significantly improving data rates and efficiency in UAV-UGV cooperation.

Contribution

It introduces a novel combined optimization algorithm incorporating SCA-based methods and a warm-start PSO with cross mutation, enhancing performance over existing approaches.

Findings

Achieves 45.8% higher minimal sum-rate than baseline PSO-CM.

Reduces computational time by 46.7% compared to PSO-CM.

UAVs adapt trajectories to avoid obstacles and optimize proximity to UGVs.

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.