WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence

TL;DR

WirelessLLM is a framework that adapts large language models to the wireless communication domain, enhancing their ability to understand, analyze, and solve complex wireless network problems through domain-specific techniques.

Contribution

The paper introduces WirelessLLM, a novel framework with principles and technologies for customizing LLMs to wireless networks, addressing domain-specific challenges.

Findings

Demonstrated practical benefits through three case studies.

Identified key principles: knowledge alignment, fusion, and evolution.

Outlined future research directions for wireless LLMs.

Abstract

The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed, configured, and managed. Recent advancements in Large Language Models (LLMs) have sparked interest in their potential to revolutionize wireless communication systems. However, existing studies on LLMs for wireless systems are limited to a direct application for telecom language understanding. To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks. We first identify three foundational principles that underpin WirelessLLM: knowledge alignment, knowledge fusion, and knowledge evolution. Then, we investigate the enabling technologies to build WirelessLLM, including prompt engineering, retrieval augmented generation, tool usage, multi-modal pre-training, and domain-specific fine-tuning. Moreover, we present three case studies to demonstrate the practical applicability and benefits of WirelessLLM for solving typical problems in wireless networks. Finally, we conclude this paper by highlighting key challenges and outlining potential avenues for future research.