Agentic AI for Embodied-enhanced Beam Prediction in Low-Altitude Economy Networks
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 ...
| Authors | Min Hao, Zhizhuo Li, Zirui Zhang et al. |
|---|---|
| Primary category | cs.NI |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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