In this letter, we use large language models (LLMs) to develop a high-performing and robust beam prediction method. We formulate the millimeter wave (mmWave) beam prediction problem as a time series forecasting task, where the historical observations are aggregated through cross-variable attention and then transformed into text-based representations using a trainable tokenizer. By leveraging the prompt-as-prefix (PaP) technique for contextual enrichment, our method harnesses the power of LLMs to predict future optimal beams. Simulation results demonstrate that our LLM-based approach outperforms traditional learning-based models in prediction accuracy as well as robustness, highlighting the significant potential of LLMs in enhancing wireless communication systems.
View on arXiv@article{sheng2025_2408.08707,
title={ Beam Prediction based on Large Language Models },
author={ Yucheng Sheng and Kai Huang and Le Liang and Peng Liu and Shi Jin and Geoffrey Ye Li },
journal={arXiv preprint arXiv:2408.08707},
year={ 2025 }
}