Beam Prediction based on Large Language Models

• URL: http://arxiv.org/abs/2408.08707v2
• Date: Wed, 12 Feb 2025 13:29:11 GMT
• Title: Beam Prediction based on Large Language Models
• Authors: Yucheng Sheng, Kai Huang, Le Liang, Peng Liu, Shi Jin, Geoffrey Ye Li,
• Abstract summary: We formulate the millimeter wave (mmWave) beam prediction problem as a time series forecasting task. We transform historical observations into text-based representations using a trainable tokenizer. Our method harnesses the power of LLMs to predict future optimal beams.
• Score: 51.45077318268427
• License:
• Abstract: 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.

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