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

TL;DR

The paper investigates the use of prompt engineering to deploy large language models in wireless networks, aiming to avoid costly pre-training and fine-tuning. It analyzes LLM fundamentals and contrasts four deployment avenues, arguing that prompt engineering offers resource-efficient, flexible, and fast deployment for resource-constrained devices, while noting its reliance on high-quality prompts and potential need for supplementary techniques. It then proposes two novel prompting schemes—iterative prompting for network optimization and self-refined prompting for network prediction—and demonstrates through case studies that these prompts can achieve competitive performance with DRL and LSTM baselines without dedicated training. The work also discusses practical challenges such as context window limitations and security concerns, and outlines future directions toward more integrated tasks like RIS and ISAC for real-time, on-device network management. Overall, the paper provides a practical, prompt-based toolkit for enabling LLM-driven wireless networks with reduced computational burden and rapid adaptation.

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.

Figures (4)

• Figure 1: Illustration of different prompting techniques.
• Figure 2: Iterative prompting schemes for wireless network optimization
• Figure 3: Self-refined prompting for network traffic prediction. As an example, by following prediction tasks framed in a question-answer format, the LLM is guided to predict hourly traffic prediction for 2013-11-08, using hourly historical traffic from 2013-11-07. Due to space constraints, we show traffic for selected hours only, while hourly traffic is fully utilized during interactions with the LLM. The specific feedback related to initial predictions is incorporated into the feedback prompt. The LLM further refines its predictions by following the actionable steps outlined in the refinement prompt. The process of feedback generation and prediction refinement is iteratively conducted until the prediction performance converges, whereas the inference is completed without engaging in the feedback generation and prediction refinement.
• Figure 4: Performance comparison in network optimization and traffic prediction case studies.