Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning

TL;DR

This paper presents a novel two-stage generative AI framework for accurate temporal spectrum mapping in low-altitude networks, combining advanced reconstruction and multi-agent trajectory optimization.

Contribution

It introduces a dual-stage framework with RecMAE for spectrum map reconstruction and MADP for UAV trajectory planning, addressing temporal and spatial complexities in LAENets.

Findings

Achieves 57.35% reduction in reconstruction error compared to traditional methods.

Attains 88.68% error reduction over deep learning baselines.

Enhances spectrum map accuracy and UAV deployment efficiency.

Abstract

This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely heavily on wireless communication technologies while facing challenges, including spectrum congestion and dynamic environmental interference. Traditional spectrum cartography methods have limitations in handling the temporal and spatial complexities inherent to these networks. Addressing these challenges, the proposed framework first employs a Reconstructive Masked Autoencoder (RecMAE) capable of accurately reconstructing spectrum maps from sparse and temporally varying sensor data using a novel dual-mask mechanism. This approach significantly enhances the precision of reconstructed radio frequency (RF) power maps. In the second stage, the Multi-agent Diffusion Policy (MADP) method integrates diffusion-based reinforcement learning to optimize the trajectories of dynamic UAV sensors. By leveraging temporal-attention encoding, this method effectively manages spatial exploration and exploitation to minimize cumulative reconstruction errors. Extensive numerical experiments validate that this integrated GenAI framework outperforms traditional interpolation methods and deep learning baselines by achieving 57.35% and 88.68% reconstruction error reduction, respectively. The proposed trajectory planner substantially improves spectrum map accuracy, reconstruction stability, and sensor deployment efficiency in dynamically evolving low-altitude environments.