Beam Prediction based on Large Language Models

• URL: http://arxiv.org/abs/2408.08707v1
• Date: Fri, 16 Aug 2024 12:40:01 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: Millimeter-wave (mmWave) communication is promising for next-generation wireless networks but suffers from significant path loss. Traditional deep learning models, such as long short-term memory (LSTM), enhance beam tracking accuracy however are limited by poor robustness and generalization. In this letter, we use large language models (LLMs) to improve the robustness of beam prediction.
• Score: 51.45077318268427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Millimeter-wave (mmWave) communication is promising for next-generation wireless networks but suffers from significant path loss, requiring extensive antenna arrays and frequent beam training. Traditional deep learning models, such as long short-term memory (LSTM), enhance beam tracking accuracy however are limited by poor robustness and generalization. In this letter, we use large language models (LLMs) to improve the robustness of beam prediction. By converting time series data into text-based representations and employing the Prompt-as-Prefix (PaP) technique for contextual enrichment, our approach unleashes the strength of LLMs for time series forecasting. Simulation results demonstrate that our LLM-based method offers superior robustness and generalization compared to LSTM-based models, showcasing the potential of LLMs in wireless communications.

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