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

2024-10-27

AI Summary

Deploying large language models (LLMs) on resource-constrained wireless devices faces challenges including excessive parameter counts, limited training data, and high fine-tuning costs. To address these, this work proposes a parameter-free lightweight paradigm centered on prompt engineering: it designs iterative prompts for network optimization tasks, integrates self-refinement and chain-of-thought reasoning to enhance prediction accuracy, and leverages in-context learning with domain-specific modeling for zero-shot or few-shot adaptation—bypassing both pretraining and fine-tuning. This approach significantly reduces computational overhead and data dependency. Evaluated across multiple real-world wireless scenarios, the method achieves performance comparable to conventional machine learning models while improving deployment flexibility and real-time responsiveness. It establishes a scalable, edge-compatible pathway for LLM integration in intelligent wireless systems.

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

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.

Problem

• Large Language Models
• Resource-limited Wireless Devices
• Data Scarcity

Innovation

• Prompting Techniques
• Wireless Network Optimization
• Efficient Resource Utilization