Agentic AI for Embodied-enhanced Beam Prediction in Low-Altitude Economy Networks

TL;DR

This paper introduces an agentic AI framework with a multi-agent reasoning architecture and a hybrid multimodal model for accurate beam prediction in UAV mmWave communications, addressing high mobility challenges.

Contribution

It proposes a novel multi-agent collaborative reasoning architecture and a hybrid multimodal model system for improved beam prediction in UAV scenarios.

Findings

Achieves up to 96.57% top-1 accuracy in simulations.

Demonstrates robustness across diverse data conditions.

Enhances beam prediction in highly mobile UAV environments.

Abstract

Millimeter-wave or terahertz communications can meet demands of low-altitude economy networks for high-throughput sensing and real-time decision making. However, high-frequency characteristics of wireless channels result in severe propagation loss and strong beam directivity, which make beam prediction challenging in highly mobile uncrewed aerial vehicles (UAV) scenarios. In this paper, we employ agentic AI to enable the transformation of mmWave base stations toward embodied intelligence. We innovatively design a multi-agent collaborative reasoning architecture for UAV-to-ground mmWave communications and propose a hybrid beam prediction model system based on bimodal data. The multi-agent architecture is designed to overcome the limited context window and weak controllability of large language model (LLM)-based reasoning by decomposing beam prediction into task analysis, solution planning, and completeness assessment. To align with the agentic reasoning process, a hybrid beam prediction model system is developed to process multimodal UAV data, including numeric mobility information and visual observations. The proposed hybrid model system integrates Mamba-based temporal modelling, convolutional visual encoding, and cross-attention-based multimodal fusion, and dynamically switches data-flow strategies under multi-agent guidance. Extensive simulations on a real UAV mmWave communication dataset demonstrate that proposed architecture and system achieve high prediction accuracy and robustness under diverse data conditions, with maximum top-1 accuracy reaching 96.57%.