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

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%.

Figures (7)

• Figure 1: UAV-to-ground mmWave beam prediction scenarios and reasoning architecture. Part A illustrates the beam prediction scenario, which includes a mmWave base station equipped with high-definition cameras and UAVs equipped with beam transceivers.Part B presents the architecture and workflow of joint reasoning and beam prediction through the cooperation of the multi-agent and the hybrid model. Inter-agent interactions and reasoning are carried out sequentially following the numbered steps.
• Figure 2: Components and Workflow of Hybrid Beam Prediction Model System. Numeric and image data of UAVs sensed by mmWave base station are processed by encoders for feature extraction and then passed to a decoder to obtain beam prediction results. Specific data-flow switching strategies and encoder parameters are obtained through multi-agent interactive reasoning.
• Figure 3: Performance of TAA in format accuracy and parameter accuracy.
• Figure 4: Relationship between SPA reasoning iterations and reasoning results.
• Figure 5: Performance of CAA in format accuracy and parameter accuracy.