WirelessLLM:Empowering Large Language Models Towards Wireless Intelligence

作     者：Jiawei Shao Jingwen Tong Qiong Wu Wei Guo Zijian Li Zehong Lin Jun Zhang Jiawei Shao;Jingwen Tong;Qiong Wu;Wei Guo;Zijian Li;Zehong Lin;Jun Zhang

作者机构：Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyHong Kong 999077China 

出 版 物：《Journal of Communications and Information Networks》 (通信与信息网络学报（英文）)

年 卷 期：2024年第9卷第2期

页      面：99-112页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

基　　金：supported by Hong Kong Research Grants Council under the Areas of Excellence Scheme Grant AoE/E-601/22-R NSFC/RGC Collaborative Research Scheme Grant CRS HKUST603/22 

主　　题：large language models multi-modal models wireless communications power allocation spectrum sensing protocol understanding 

摘      要：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.