Large Language Models for Wireless Networks: An Overview from the Prompt Engineering Perspective

TL;DR

This paper explores how prompt engineering with large language models can be effectively applied to wireless networks, enabling network tasks without extensive model training or fine-tuning, thus offering flexible and resource-efficient solutions.

Contribution

It introduces novel prompt schemes for network optimization and prediction, demonstrating their effectiveness compared to traditional machine learning methods.

Findings

Prompt schemes achieve comparable performance to conventional ML.

Prompting methods reduce the need for model training and fine-tuning.

Proposed methods offer higher deployment flexibility and lower computational requirements.

Abstract

Recently, large language models (LLMs) have been successfully applied to many fields, showing outstanding comprehension and reasoning capabilities. Despite their great potential, LLMs usually require dedicated pre-training and fine-tuning for domain-specific applications such as wireless networks. These adaptations can be extremely demanding for computational resources and datasets, while most network devices have limited computation power, and there are a limited number of high-quality networking datasets. To this end, this work explores LLM-enabled wireless networks from the prompt engineering perspective, i.e., designing prompts to guide LLMs to generate desired output without updating LLM parameters. Compared with other LLM-driven methods, prompt engineering can better align with the demands of wireless network devices, e.g., higher deployment flexibility, rapid response time, and lower requirements on computation power. In particular, this work first introduces LLM fundamentals and compares different prompting techniques such as in-context learning, chain-of-thought, and self-refinement. Then we propose two novel prompting schemes for network applications: iterative prompting for network optimization, and self-refined prompting for network prediction. The case studies show that the proposed schemes can achieve comparable performance as conventional machine learning techniques, and our proposed prompting-based methods avoid the complexity of dedicated model training and fine-tuning, which is one of the key bottlenecks of existing machine learning techniques.