Beam Prediction based on Large Language Models

2024-08-16

arXiv.org

AI Summary

This work addresses the low accuracy and poor robustness of beam prediction in millimeter-wave (mmWave) communications. To this end, it pioneers the integration of large language models (LLMs) into this task, proposing a novel text-based beam prediction paradigm grounded in time-series modeling. Methodologically, it introduces a trainable tokenizer to encode historical channel observations as textual sequences and employs a Prompt-as-Prefix (PaP) mechanism for context-aware sequence generation. Crucially, it proposes cross-variable attention aggregation, eliminating reliance on explicit channel structural priors. Experiments demonstrate that the method achieves over 15% higher prediction accuracy than CNN- and LSTM-based baselines in simulations, while significantly improving robustness under low signal-to-noise ratio (SNR) conditions and dynamic environments. This work establishes a new pathway for leveraging LLMs to enhance intelligence at the wireless physical layer.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Beam prediction using large language models
• Time series forecasting in mmWave systems
• Enhancing wireless communication robustness and accuracy

Innovation

• LLMs for beam prediction
• Cross-variable attention aggregation
• Prompt-as-prefix technique