无线通信的大型人工智能模型.pdf

1、Large Language Models for Intelligent Wireless CommunicationsProfessor Geoffrey Ye LiDepartment of Electrical and Electronics EngineeringImpeial College LondonLondon,UKContributed byProf.Le Liang,Southeast UniversityProf.Hao Ye,University of Califonia,Santa CruzOutlineI.MotivationII.Adapting LLMs to

2、 Wireless Tasks III.Wireless Foundation ModelsIV.Agentic LLMs for Wireless CommunicationV.Challenges and Opportunities2DL in Physical Layer Communications3Z.-J.Qin,H.Ye,G.Y.Li,and B.-H.Juang,“Deep learning in physical layer communications,”IEEE Wireless Commun.,vol.26,no.2,pp.93-98,April 2019.(2022

3、IEEE ComSoc Fred W.Ellersick Prize Paper Award)Example 1:H.Ye,G.Y.Li,and B.-H.F.Juang,“Power of deep learning for channel estimation and signal detection inOFDM systems,”IEEE Wireless Commun.Lett.,vol.7,no.1,pp.114 117,Feb.2018.Example 2:H.-T.He,C.-K.Wen,S.Jin,and G.Y.Li,“Model-driven deep learning

4、for MIMO detection,”IEEE Trans.Signal Process.,vol.68,pp.1702-1715,March 2020.Example 3:H.Xie,Z.Qin,G.Y.Li,and B.-H.Juang,“Deep learning enabled semantic communication systems,”IEEETrans.Signal Process.vol.69,pp.2663-2675,2021,Apr.2021.(2023 IEEE SPS Best Paper Award)Example 1Example 2Example 3DL fo

5、r Wireless Resource Allocation L.Liang,H.Ye,G.-D.Yu,and G.Y.Li,“Deep learning based wireless resource allocation with application invehicular networks,”Proc.IEEE,vol.108,no.2,pp.341-356,Feb.2020.H.Ye,G.Y.Li,B.-H.F.Juang,“Deepreinforcement learning based resourceallocationfor V2Vcommunications,”IEEE

6、Trans.Veh.Tech.,vol.68,no.4,pp.3163-3173,April 2019.4Why LLMs for Intelligent Communications?5 Limitations of Traditional AI Models Limited generalization Task-specific architectures Inefficiency in handling cross-task reasoning Advantages of Large Language Models(LLMs)Adaptability Scalability Zero-