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

arXiv: https://arxiv.org/abs/2603.11392 · HTML: https://mlsanity.com/paper/2603.11392/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment](https://mlsanity.com/paper/2406.01638/) (2024)
- [ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data](https://mlsanity.com/paper/2412.11376/) (2024)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/2102.09527/) (2021)
- [Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets](https://mlsanity.com/paper/2111.07574/) (2021)
- [Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions](https://mlsanity.com/paper/2508.18725/) (2025)
- [Beam Prediction based on Large Language Models](https://mlsanity.com/paper/2408.08707/) (2024)
- [Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions](https://mlsanity.com/paper/2504.21583/) (2025)
- [Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction](https://mlsanity.com/paper/2309.11811/) (2023)
- [Integrated Sensing and Communications Towards Proactive Beamforming in mmWave V2I via Multi-Modal Feature Fusion (MMFF)](https://mlsanity.com/paper/2310.02561/) (2023)
- [A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking](https://mlsanity.com/paper/2104.09204/) (2021)
- [Sensing-Aided 6G Drone Communications: Real-World Datasets and Demonstration](https://mlsanity.com/paper/2412.04734/) (2024)
- [Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach](https://mlsanity.com/paper/2505.17637/) (2025)
- [Tracking mm-Wave Channel Dynamics: Fast Beam Training Strategies under Mobility](https://mlsanity.com/paper/1612.07957/) (2016)
- [Joint Optimization of UAV-Carried IRS for Urban Low Altitude mmWave Communications with Deep Reinforcement Learning](https://mlsanity.com/paper/2501.02787/) (2025)
- [An Accurate Beam-Tracking Algorithm with Adaptive Beam Reconstruction via UAV-BSs for Mobile Users](https://mlsanity.com/paper/2404.13262/) (2024)

## Abstract (arXiv)

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%.