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1、1/92Executive SummaryDriven by the joint effort of key technologies such as big data and cloudcomputing,a sizable number of the generative pre-trained transformer(GPT)largemodels,represented by ChatGPT,have emerged,showing highly creative contentgeneration capabilities and providing highly intellige

2、nt human-computer interactionexperience.For a long time,there have been many technical problems incommunication that are difficult to model accurately or solve efficiently usingtraditional methods.Meanwhile,GPT demonstrates the potential to improve theperformance of information communication service

3、s and intelligent autonomousnetworks.In addition,the rapid development and broad applications of GPT also needto be supported by a communication network with large bandwidth,low latency,andhigh reliability.Therefore,from the perspective of communication practitioners,this white paperexplores the int

4、errelationship between GPT and communication.Firstly,Chapter 1sketches the concept,development process,and research status of GPT large models.Secondly,Chapter 2 discusses the new applications of GPT in the communicationindustry,and the position of GPT in network intelligent autonomy.Thirdly,Chapter

5、 3explores how the communication networks enable the broad applications of GPT,andgives a typical idea of future network design.Moreover,Chapter 4 analyzes theprocess of GPT and communication from independent evolution to collaborativedevelopment,as well as applications of“6G+GPT”empowering the digi

6、taltransformation of industries.In addition,Chapter 5 points out the five most obviousproblems and challenges in the integration process of“GPT+Communication”andprovides some solutions.Subsequently,Chapter 6 puts forward several suggestions onhow GPT and the communication industry can develop togeth

7、er,as well as theprospects for the future.Finally,Chapter 7 concludes this white paper.2/92ContentsExecutive Summary.10 Preface.41.GPT Leads the Tide of Artificial Intelligence Development.81.1.Basic Concepts of GPT.81.1.1Generative Pre-trained Transformer.81.1.2Large Model.91.1.3TransformerArchitec

8、ture.111.2.Development History of GPT.131.3.Current Research Status of GPT.151.3.1Forein Research Status.161.3.2Domestic Research Status.181.3.3International Organizations.192.GPT Empowers the Communication Industry.202.1.GPT Stimulates NewApplications and Reforms in Communication.202.1.1Intelligent

9、 Customer Service.222.1.2Automation Simulation.232.1.3Enhanced Semantic Communication.242.1.4Reshaping the Field of Chip Design.252.2.GPT Promotes Intelligent Autonomy in Communication Networks.262.2.1GPT Reshapes Network Planning.282.2.2GPT Enhances Slicing Deployment.292.2.3GPT Simplifies Network

10、Operations and Maintenance.302.2.4GPTAccelerates Network Optimization.323.Communication Networks Enable GPT Ubiquitous Applications.353.1 Communication Networks Guarantee the Landing of GPTApplications.353.2 Future Network Technology Supports GPTApplications.383.2.1 TypicalApproaches to Future Netwo

11、rk Design.383.2.2 6G Network with Native Support for GPTApplications.393.3 New Network Architecture Supports GPT Capability Sinking.413.3.1Adaptive Slicing.413.3.2 Distributed Learning.433.3.3 Edge Intelligence.434.Collaborative Development of GPT and Communication.464.1.GPT and Communication from I

12、ndependent Evolution to Close Integration.464.1.1Trends in the Integration of GPT and Communication.464.1.2Integration of GPT and 5G Networks.474.2.Integration and Development of GPT with 6G Communication Networks.484.2.1GPT Supports Massive Data Processing.494.2.2GPT Promotes Network Self-Service.5

13、03/924.2.3GPTAssists in Network Resource Orchestration.504.2.4GPT Constructs Network Endogenous Security.504.3.“6G+GPT”Empowers Industry Digital Transformation.514.3.1“6G+GPT”Empowers Smart Industry.524.3.2“6G+GPT”Empowers Smart Healthcare.534.3.3“6G+GPT”Empowers Smart Transportation.534.3.4“6G+GPT”

14、Empowers Smart Agriculture.544.3.5“6G+GPT”Empowers Smart Home.554.3.6“6G+GPT”Empowers Digital Entertainment.555.Problems Faced by the Development of“GPT+Communication”Integration565.1.Scarcity of High-Quality Training Data in Communication Leads to Poor Accuracy andGeneralization of Specialized Mode

15、ls.575.2.Insufficient On-Device Computing Power and Hardware Resources Pose Challenges toLightweight Deployment of Large Models.605.3.Difficulties in Cloud-Edge-Terminal Heterogeneous Network Collaboration Lead to PoorStability Performance of Large Models.625.4.Server Interconnection Bandwidth Bottl

16、enecks Result in Long Training Time and LowInference Efficiency.655.5.Lagging Legal Regulations Related to Large Models Result in High Risks of Security,Privacy,and Ethical Issues.676.Development Recommendations and Future Prospects.716.1.Development Recommendations.716.1.1Accelerating the Construct

17、ion of AI Computing Power and Providing InfrastructureSupport.716.1.2Strengthening Joint Training of Schools and Enterprises to Fill the Gap inInnovative Talents.746.1.3Accelerating the Formulation of Relevant Policies and Establishing Platforms toGuide Development.766.2.Future Prospects.786.2.1Brea

18、kthroughs in Core Technologies and Significant Enhancement of KeyCapabilities.786.2.2Continuous Improvement in System Construction and Rapid Development of theDigital Economy.806.2.3Expansion ofApplication Scenarios,Gradual Integration and Symbiosis.827.Conclusion.84References.85Abbreviations.90Ackn

19、owledgments.924/920 PrefaceIn recent years,as Artificial Intelligence(AI)technology has continued toadvance,particularly in the areas of reinforcement learning,large models,andgenerative content,various industries have been actively exploring its applications.Atthe end of November 2022,OpenAI releas

20、ed the rapidly popularized chatbotChatGPT,which possesses astonishing natural language understanding and generationcapabilities,attracting widespread attention from society.Subsequently,in March2023,the launch of the upgraded version GPT-4 multimodal large model reignitedenthusiasm for generative AI

21、,leading to the emergence of numerous large models inquick succession.Sincetheinceptionoftext-basedconversationalinteractions,GPThasprofoundly impacted peoples production and lives within a few short years,bringingabout significant changes.Many people believe that it will continue to bringdisruptive

22、 changes.Bill Gates pointed out that large models represent the mostrevolutionary technological advancement in over 40 years;NVIDIA CEO JensenHuang likened the emergence of large models to the“iPhone moment”of AI;BaiduCEO Robin Li proposed that large models are prepared to change the world at the202

23、3 Zhongguancun Forum.From the ripples caused by ChatGPT to the global waveit unleashed,GPT large models have become one of the most discussed topics today,signaling a crucial turning point in the development of generative AI;the year 2023will also undoubtedly leave a significant mark in the history

24、ofAI development.As an industry facilitating information exchange and transmission amonghumans,nature,and machines,the communication industry is closely intertwinedwith the development of large model technology.The communication industry itselfhas a high degree of digitalization and needs to handle

25、complex data.The introductionof GPT can streamline a significant amount of work,bringing about significantcapacity enhancements for communication operators,particularly in the realms ofnetwork operations and maintenance(O&M)and service delivery,making them moreintelligent.In the era of large models,

26、with the advancement of GPT technology,thedemand for computing power,data,and algorithms will experience explosive growth,requiring communication infrastructure to provide support.In the future,how GPTempowers the communication industry and how the communication industry supports5/92GPTarequestionst

27、hateverycommunicationprofessionalshouldearnestlycontemplate.Therefore,this white paper is based on the development history and latestresearch advancements of GPT large models.On the one hand,it elaborates on theinnovative applications of GPT within the communication industry in specificscenarios.On

28、the other hand,it investigates how future communication networksprovide native support for GPT in terms of architecture and key technologies.Subsequently,combining GPT with communication,it proposes a roadmap for thedigital and intelligent transformation of key industries through their collaborative

29、development,while also pointing out the problems and challenges in the integrationand development process.In response to these issues,corresponding developmentrecommendations and prospects are provided.Finally,the whole content of this whitepaper is summarized.The complete chapter structure of this

30、white paper is illustratedin Figure 0-1 below.6/92Figure 0-1 White Paper Chapter Structure DiagramThis white paper was jointly organized and authored by the Beijing Institute ofTechnology,with participation from 18 entities,including the three major telecomoperators(China Mobile,China Unicom,and Chi

31、na Telecom),seven top-tieruniversities,three renowned enterprises,and five leading research institutes in theindustry.Spanning over eight months,the process involved the in-depth participationof over 50 experts and scholars,from conducting research and tracking the cutting-edge status of GPT large m

32、odels to exploring the relationship between GPT andcommunication,conceptualizing the outline of the white paper,arranging specificchapter content,and assigning writing tasks.It underwent more than twenty rounds ofdiscussions and revisions before reaching its completion.During this period,somepartici

33、pating entities also successfully collaborated to apply for an international7/92cooperation project from the Ministry of Science and Technology of the PeoplesRepublicofChina,titled“ResearchonKeyTechnologiesofIntegratedMultidimensional Intelligent Orchestration in Cloud Computing Networks Based onLar

34、ge Models,”thereby better supporting the completion of this white paper.We believe that AI technology is still in a rapidly developing stage,and theintegration and mutual support between GPT large models and communicationnetworks can continually expand innovative application scenarios and improveeco

35、system development,thus jointly promoting technological progress and thedevelopment of various industries.8/921.GPT Leads the Tide of Artificial Intelligence DevelopmentWith the advancement of AI and deep learning technologies,the concept of“large models”has come into focus,with ChatGPT being the mo

36、st notable.OnNovember 30,2022,OpenAI officially released the AI chatbot ChatGPT,whichrepresents Artificial Intelligence Generated Content(AIGC)in the field of naturallanguage.Its powerful capabilities have changed the way many people work and live,sparking a new wave of AI globally and attracting wi

37、de attention from both industryand academia.On March 14,2023,the officially released GPT-4 underwent furtherupgrades,significantly relaxing text input restrictions,improving answer accuracy,and even enabling direct input of images to generate lyrics,creative texts,etc.,withstyle variations,once agai

38、n showcasing the impact of generative AI.On November 7,2023,at the first-ever OpenAI DevDay,OpenAI CEO Altman showcased GPT-4Turbo to the world.As the latest version of GPT,it has been updated in areas such asdata quality,image processing,and speech conversion,bringing developers and usersmore possi

39、bilities and opportunities.So,what are ChatGPT and GPT?What development journey have theyundergone?And how should they be understood and applied?This chapter will startwith an exploration of GPT large models,introducing their basic concepts,development history,and current research status to provide

40、readers with acomprehensive and in-depth understanding of GPT.1.1.Basic Concepts of GPT1.1.1Generative Pre-trained TransformerGPT stands for Generative Pre-trained Transformer,originating from the fieldsof deep learning and natural language processing(NLP).Over the past few years,with the advancemen

41、t of computing power and the emergence of big data,significantbreakthroughs have been made in the field of NLP.GPT,as an integration of a seriesof NLP technologies,emerged in such a context,as shown in Figure 1-1.G:Generative.This indicates that GPT has the ability to spontaneously generatecontent.P

42、:Pre-trained.This indicates that GPT has undergone pre-training and is readyfor immediate use.9/92T:Transformer.This indicates that GPT is based on the Transformer architecturefor language modeling.Figure 1-1 Meaning of GPTIn 2017,the Google team first proposed the Transformer model based on theSelf

43、-Attention Mechanism(SAM)and applied it to NLP1.OpenAI applied thistechnology and released the earliest generation of large models,GPT-1,in 2018.Sincethen,the parameter size of each generation of GPT models has grown explosively.The parameter size of GPT-2,released in February 2019,was 1.5 billion,w

44、hile GPT-3,released in May 2020,directly reached 175 billion.The meteoric rise of ChatGPT was not by chance.It is the result of the efforts ofmany people and a long period of evolution.To understand the development of GPT,one should first grasp the concept of large models and Transformer architectur

45、e.1.1.2Large ModelGenerally,before ChatGPT,the AI models that received public attention weremainly used for single tasks.For example,“AlphaGo”,which ignited the entire AImarket and prompted its explosive development,defeated Go world champion LeeSedol in the“Man vs.Machine”match in 2016,based on glo

46、bal Go game records.However,fundamentally,these AI data models,which focus on specific tasks,canonly be called“small models”compared to ChatGPT.Large models refer to machine learning models with huge parameter scales andcomplexity.The term usually refers to Large Language Models(LLMs).A languagemode

47、l is an AI model that,after training,can understand and generate humanlanguage,and“large”means that the models parameters are very large relative to“small models.”As shown in Figure 1-2,this evolutionary tree traces the development history of10/92large models in recent years,highlighting some of the

48、 most well-known models,withmodels on the same branch being more closely related2.Solid squares representopen-source models,while hollow squares represent closed-source models.Non-Transformer models are shown in gray,and among Transformer-based models,Encoder models are in the pink branch,Decoder mo

49、dels are in the blue branch,andEncoder-Decoder models are in the green branch.Figure 1-2 Evolutionary Tree of Large ModelsBased on this evolutionary tree diagram,we can conclude that Decoder-onlymodels are gradually becoming the dominant models in LLM development,andOpenAI continues to maintain its

50、leading position in LLM.Meta has madeoutstanding contributions to open-source and LLM research,but there is a trendtowards closed-source development after the launch of GPT-3.In addition,manycompanies and institutions are still actively exploring Encoder-Decoder models,suchas Google.Currently,major

51、institutions abroad that release large models include OpenAI,Anthropic,Google,and Meta,with model parameter scales mainly in the tens andhundreds of billions.Up to now,the top GPT large models abroad include ChatGPT,Claude,Bard,and Llama.Among them,after Google released the latest native11/92multimo

52、dal large model Gemini,Bard was officially renamed Gemini.In this globally competitive arena,China is also keeping pace,developing manylarge models,including Tencents“Hybrid,”Alibabas“Tongyi Qianwen,”Huaweis“Pangu,”and China Mobiles“Jiutian”series.Data shows that as of October 2023,there are a total

53、 of 254 domestic companies,universities,and research institutes withlarge models of over 1 billion parameters,indicating that the“battle of the hundredmodels”is transitioning from the previous stage of“being born”to a new stage of“being used.”Figure 1-3 shows some of the large models developed by do

54、mestic andforeign companies currently.Figure 1-3 Various Types of Large Models1.1.3TransformerArchitectureThe Transformer architecture is a crucial foundation of GPT,which is a neuralnetwork architecture based on the SAM and widely used in large models in the fieldof NLP.Its core components are the

55、Encoder and Decoder.The Encoder encodesinput text into a series of vectors,while the Decoder decodes these vectors one by oneinto output text.Before the introduction of Transformer,the mainstream models in theNLP field were Recurrent Neural Networks(RNNs),which used recursion andconvolutional neural

56、 networks for language sequence transformation.In June 2017,the Google Brain team published a paper titled Attention is All YouNeed at the top AI conference NeurIPS,proposing a new network architecture calledTransformer.It is entirely based on the SAM,abandoning recursion and convolution.After only

57、12 hours of training on eight P100 Graphics Processing Units(GPUs),Transformer achieved higher translation quality1,showcasing excellent parallelism12/92and becoming the most advanced LLM at the time.Figure 1-4 illustrates the network structure of the Transformer.It consists of aseries of Encoders a

58、nd Decoders,each comprising multi-head attention layers and all-inclusive connected feedforward networks.GPT,similar to the Decoder part ofTransformer,is an autoregressive model.Figure 1-4 Transformer Network Structure DiagramThe core component in the Transformer is the multi-head attention mechanis

59、mmodule,as shown in Figure 1-5.It requires three specified inputs:Q(Query),K(Key),and V(Value).Then,it calculates the similarity between each pair of Q and K andweights each V based on the similarity to obtain the attention calculation result.13/92Figure 1-5 Multi-Head Attention Mechanism ModuleThe

60、multi-head attention mechanism does not calculate attention only once butdivides the input into smaller blocks and then calculates the scaled dot-productattention in parallel on each subspace.This design allows each attention mechanismto optimize different feature parts of each word,balancing the bi

61、ases that may arisefrom the same attention mechanism and enabling the model to capture semanticinformation at different levels,thereby enhancing the models expressive power andimproving its effectiveness.1.2.Development History of GPT14/92Figure 1-6 Development History of GPTThe development history

62、of GPT can be divided into two stages.BeforeChatGPT,the emphasis was on continuously increasing the basic scale of largemodels and enhancing new capabilities.ChatGPT and GPT-4,on the other hand,focus more on reinforcement learning from human feedback to understand humanintent and provide better serv

63、ices,as shown in Figure 1-6.June 2018:OpenAI published the paper Improving Language Understandingby Generative Pre-training and officially released GPT-13.Basic approach:Generative pre-training(unsupervised)+downstream taskfine-tuning(supervised).Based on a unidirectional Transformer language model

64、with a decoderstructure,consisting of 12 layers.117 million parameters,5 GB training data,relatively limited model size andcapabilities.Context window:512 tokens.February 2019:OpenAI published the paper Language Models areUnsupervised Multitask Learners,proposing that language models are unsupervise

65、dmultitask learners,and GPT-2 was born4.Basic approach:Removing supervision,retaining only unsupervised learning.48-layer Transformer structure.15/921.5 billion parameters,and the training data volume increased to 40 GB.Context window:1024 tokens.May 2020:OpenAI published the paper Language Models a

66、re Few-ShotLearners and introduced the GPT-3 model5.Basic approach:Unsupervised learning+in-context learning.96-layer multi-head Transformer.The number of parameters increased to 175 billion,trained on 45 TB of textdata.Context window:2048 tokens.March 2022:OpenAI once again published the paper Trai

67、ning LanguageModels to Follow Instructions with Human Feedback,introducing ReinforcementLearning from Human Feedback(RLHF),and launched the InstructGPT model6.Basic approach:RLHF+fine-tuning training.Enhanced human adjustment of model output.Results ranked in a more understandable manner.ChatGPT is

68、a derivative of InstructGPT,and the two have the same modelstructure and training method.The only difference is the way they collect data.ChatGPT focuses more on interaction in the form of dialogue.March 2023:OpenAI released the multimodal pre-trained large model GPT-4,once again undergoing signific

69、ant upgrades.Basic approach:Multimodal.Context window:8195 tokens.1.8 trillion parameters,13 trillion token training data.Powerful image recognition capabilities.Although the current capabilities of GPT-4 in real-world scenarios may notmatch those of humans,it has demonstrated significantly superior

70、 abilities in variousprofessional and academic exams.Even SAT scores(which can be understood asscores for the U.S.college admissions test)of GPT-4 have surpassed those of 90%oftest takers,reaching the level required for admission to top universities such asHarvard and Stanford.1.3.Current Research S

71、tatus of GPTOn October 12,2023,the analysis company stateof.ai released the State of AIReport 2023.The report pointed out that OpenAIs GPT-4 remains the most powerful16/92LLM globally.Generative AI has propelled advancements in life sciences and hasbeen a savior for the venture capital industry7.Lar

72、ge models continue to achievetechnological breakthroughs,especially in the field of life sciences,makingsignificant progress in molecular biology and drug discovery.On December 14,2023,Nature announced ten people in 2023.Notably,thechatbot ChatGPT,due to its dominance of various news headlines in 20

73、23 andprofound impact on the scientific community and society at large,was included as the11th“non-human member”on the list,recognizing the significant changes broughtabout by generative AI to scientific development and progress.Currently,bothdomestically and abroad,research on GPT large models cont

74、inues to deepen,withmany institutions starting to develop their own large models,and the applicationscenarios are becoming increasingly diverse.Large models represented by ChatGPThave officially ushered in the era ofAI 2.0.1.3.1Forein Research Status1United StatesIn the United States,startups like O

75、penAI and Anthropic,along with tech giantssuch as Microsoft and Google,are leading the rapid development of large models.Major companies are continually enhancing their competitiveness.Google invested$300 million in Anthropic to counter the threat posed by ChatGPT,joiningreinforcement learning from

76、artificial intelligence feedback(RLAIF)to reduce humanfeedback.In December 2022,Google published a paper titled Constitutional AI:Harmlessness from AI Feedback,introducing the AI model Claude.Buzzfeed,a USnew media giant,saw its stock price triple in two days after announcing plans to useChatGPT to

77、assist content creation.Microsoft,as the main investor in OpenAI,is alsousing ChatGPT toenhance itsproductcompetitivenessandsupplementitsprofessional knowledge and mathematical shortcomings.2United KingdomIn April 2023,the UK government announced that it would provide 100 millionin initial funding t

78、o the team responsible for building the UK version of thefoundational AI model to accelerate the development ofAI technology in the UK.TheUK government stated that this investment would be used to fund new teams jointlybuilt by the government and the industry to ensure the UKs AI“sovereigncapabiliti

79、es.”The goal of this initiative is to promote the application of safe and17/92reliable foundational models and strive to build the UK into a technological“superpower”by 2030.In addition,in response to the controversy over the applicationof large models such as GPT in AI ethics,the UK has also issued

80、 a white paper onregulatory measures and stated that regulatory agencies will next issue guidelines andrisk assessment templates to various organizations.Other tools and resources will beused to formulate specific implementation principles within the industry.EuropeIn Finland,Flowrite is an AI-based

81、 writing tool that can generate emails,messages,and other content by inputting keywords.In the Netherlands,theomnichannel communication platform MessageBird launched its own AI platformMessageBird AI,which can understand the meaning of customer information andrespond accordingly.Both are based on GP

82、T-3.Germany is also constantly catchingup in the development of large models.For example,on March 7,2023,Googlelaunched the multimodal large model PaLM-E,jointly developed by the TechnicalUniversity of Berlin and Google.In February 2024,the European generative AI unicorn Mistral AI unveiled itslates

83、t LLM,Mistral Large.With a context window of 32K tokens,this model supportsEnglish,French,Spanish,German,and Italian.As the flagship model newly launched,Mistral Large demonstrated outstanding performance in common-sense reasoning andknowledge quizzes,scoring higher overall than Gemini Pro and Claud

84、e 2,second onlyto GPT-4.South KoreaSouth Korea is also among the earliest countries to engage in large modeldevelopment.Currently,notable representatives in this field from South Korea includeNAVER,Kakao,KT,SKT,and LG.South Koreas accumulation of expertise insemiconductor chips positions it advantag

85、eously in the realm of large models.Presently,South Korean semiconductor companies are actively forming alliances totackle the computational challenges posed by large model development.By the end of2022,NAVER initiated collaboration with Samsung Electronics to develop next-generation AI chip solutio

86、ns,optimizing them based on NAVERs large model,HyperCLOVA.Moreover,South Korea has made considerable explorations in thevertical applications of large models,such as KoGPT in healthcare and Exaone inbiopharmaceuticals and intelligent manufacturing.18/92 JapanJapan,as a country with a less common lan

87、guage,faces the challenge ofinsufficient linguistic data.The earliest publicly launched NLP large model in JapanwasNTELLILINKBackOffice,introducedin 2020,capable of documentclassification,knowledge reading comprehension,and automatic summarization,among other functions.It is an application developed

88、 based on Google BERT.The more Japanese-blooded generative AIs are actually HyperCLOVA,Rinnaand ELYZA Pencil,but HyperCLOVA and Rinna also have foreign genes.HyperCLOVA,initially launched by the South Korean search giant NAVER in 2021,stands out as the first LLM specifically tailored for the Japanes

89、e.It achieved firstplace in all tracks at the dialogue system live competition held in 2021.ELYZAPencil,on the other hand,is an LLM introduced by an AI startup affiliated with the MatsuoLaboratory at the University of Tokyo,marking Japans first genuine public release ofa generativeAI product.1.3.2Do

90、mestic Research StatusMany might believe that Chinas journey with large models began with the“ERNIE Bot,”but in reality,its merely a conversational tool powered by largemodels.Large models were already introduced domestically as early as 2019.In thatyear,large models were extensively applied in drug

91、 development,prompting majortechnology companies to initiate their own large model projects.In March 2021,theBeijing Academy of Artificial Intelligence unveiled Chinas first ultra-large-scaleintelligent model system,“Wudao 1.0.”Subsequently,in April of the same year,Alibaba Group launched PLUG,the l

92、argest pre-trained language model in the Chinesecommunity,which was widely referred to as the“Chinese version of GPT-3”at thetime.In recent years,significant progress has been made domestically in the field oflarge models.From research institutions to enterprises,there has been a substantialincrease

93、 in investment in large models,leading to significant breakthroughs inalgorithms,computing power,data,and other areas.China has produced a batch ofinternationally competitive large models,widely applied across various fields.On March 16,2023,based on the ERNIE large model,Baidu released“ERNIEBot,”Ch

94、inas first ChatGPT-like product.On May 6,2023,iFLYTEK launched theChinese version of ChatGPT,“Spark Cognitive Large Model,”capable of text19/92generation,language understanding,knowledge question answering,logical reasoning,mathematical abilities,coding skills,and multimodal capabilities.1.3.3Intern

95、ational OrganizationsToday,international organizations such as the International Organization forStandardization(ISO)and the International Electrotechnical Commission(IEC)haveall carried out standard research on key terminologies.In March 2023,the EuropeanTelecommunication StandardsInstitute(ETSI)al

96、sointroducedstandardsandspecifications concerning the transparency and interpretability of AI,aiming togenerate more interpretable models while maintaining high levels of modelperformance.The specifications of the 3rdGeneration Partnership Project(3GPP)include thedeployment and usage of AI in networ

97、k architecture,covering specifications for AIalgorithms and architectures,as well as standards for the processing and managementof AI data.Currently,3GPP has four working groups engaged in standardizationefforts related to AI/machine learning(ML),including AI/ML for Air Interface,AI/ML for RAN,AI/ML

98、 for 5GS,and AI/ML for OAM.In November 2023,during the Institute of Electrical and Electronics Engineers(IEEE)“Artificial Intelligence Large Models”standard conference jointly organizedby the Shanghai AI Laboratory and SenseTime,11 entities,including the ChinaElectronics Standardization Institute,th

99、e Shanghai Artificial Intelligence Laboratory,and Huawei Cloud,jointly initiated the establishment of the IEEE Large ModelStandard Working Group.This working group will collaborate with domestic andinternational forces in the large model industry to formulate international advancedstandards in areas

100、 such as large model technical specifications,evaluation methods,safety and trustworthiness,and reliable decision-making,providing better support forglobal large model industry technological innovation and development.20/922.GPT Empowers the Communication IndustryIn Chapter 1,we introduced the conce

101、pt,development history,and currentresearch status of GPT.It can be seen that GPT has been applied in numerous fields,becoming an important transformative technology and a key force in economic andsocialdevelopment.GPT is preparedtobringaboutsignificantleapsandbreakthroughs in global industries.Curre

102、ntly,GPT has achieved the function of“communication”between humans and machines in various forms,approaching oreven surpassing the experience of chatting between individuals through text.Thisparallels the role of the communication industry in facilitating diverse forms of humaninteraction.The applic

103、ation of AI in the communication industry paves the way fornew solutions in the construction and operation of information and communicationinfrastructures.As a new peak inAI development,GPT-driven AI as a service(AiaaS)offers greater business opportunities,providing a broad stage for innovation in t

104、hecommunication industry.How GPT empowers applications in the communicationindustry and how the communication industry ensures the implementation of GPT arequestions that communication professionals must consider and address.This chapter will focus on innovative applications of GPT in the communicat

105、ionindustry,highlighting its reformative and progressive role in various segments ofcommunication.By studying methods through which GPT promotes intelligentautonomy in communication networks,we have analyzed how GPT large modelsempower communication networks from the perspectives of network planning

106、,slicingdeployment,network operation and maintenance,and network optimization.Weanticipate that the rapid development of GPT will promote the deep integration of AIand the communication industry,accelerate the construction of next-generationinformation infrastructure,and facilitate the digital trans

107、formation of the economyand society.2.1.GPT Stimulates NewApplications and Reforms in CommunicationThe emergence of diverse applications enabled by GPT has brought newimaginative spaces for various industries,as well as new opportunities and challengesfor the communication industry.GPT has transform

108、ed traditional communicationmodes and application scenarios,breaking the boundaries of human-machineinteraction to provide more intelligent,convenient,and personalized communication21/92experiences,significantly enhancing information interaction capabilities and industryapplication capabilities.GPT

109、large model can be utilized to enhance information communication servicecapabilities.First of all,its powerful natural language capabilities can be used toimprove operational service functions such as intelligent customer service,smartoperations,and fraud detection.The vast amount of data in communi

110、cation networkscan be used to train large communication network models.Furthermore,the successof GPT in natural language has spurred the development of multimodal datatechnologies such as voice and vision,which will provide important tools for thedigital transformation of all walks of life in the co

111、mmunication field.Lastly,theoperation and services of GPT-like large models require high computational powerand network support,which will,to some extent,promote the construction ofconverged computing and networking,creating conditions for the implementation andpopularization of more large models in

112、 the communication industry.Through iterative training with massive data,GPT possesses continuouslyimprovingcontextualsemanticunderstandingandinteractivecapabilities,demonstrating infinite potential in numerous application scenarios.Currently,GPTsapplications are primarily focused on generating text

113、,images,audio,video,and othermultimodal content.Applications in fields such as photography,gaming,and mediatypically involve customized development or training based on these fundamentalapplications.Examples include text generation and analysis8,software testing910,and domain-specific chatbots11,as

114、shown in Figure 2-1.22/92Figure 2-1 NewApplications of GPT in Communication2.1.1Intelligent Customer ServiceIntelligent customer service systems aim to provide efficient,flexible,andcustomizable solutions for communication operators customers,managing andmaintaining interactions between operators an

115、d customers.Combining intelligentcustomer service systems with GPT can leverage the technical advantages of both toenhance the quality and efficiency of customer service in various aspects,such asintelligent voice assistants,smart recommendations,self-service,social mediamanagement,and personalized

116、services.This meets the growing demand forpersonalized services from customers,helping companies better serve customers andenhance competitiveness and profitability.1)Enhancing semantic understanding and emotion recognition of intelligentcustomer serviceGPTs NLP capabilities address many shortcoming

117、s of intelligent customerservice systems.GPT accurately identifies the topics and keywords of user queries,23/92helping intelligent customer service systems better understand user demands andidentify user emotional states,thereby providing more accurate and personalizedservices.2)Implementing intell

118、igent service supervisionGPT can be used to automatically detect customer service dialogue content,identify potential violations or inappropriate language,such as insults,discrimination,fraud,and pre-screen potential problem dialogues,only submitting dialogue contentthat may violate the regulations

119、to manual review,to a certain extent,reducing theworkload of the auditors.By analyzing the output of GPT models,trends and patternsof various types of violations can be quickly understood and identified,and thus,regulatory authorities can improve regulatory policies and take timely measures.2.1.2Aut

120、omation SimulationGPT can reconstruct experimental processes to facilitate automation simulation.GPT is pre-trained on large amounts of text and can further generalize based oncontextual prompts.Unlike traditional workflows,it does not require changingsimulation settings parameters,underlying ML alg

121、orithms,or data formats each time.Users only need to provide parameters related to the predefined architecture,parse thecreated models,insert them into the templates prepared by GPT,and finally achieveautomation simulation through GPT.In the simulation design phase,GPT can help designers quickly pro

122、totype,enabling development teams and stakeholders to better understand the systemsworkflow and functionality,identify issues,and improve requirements in advance.Based on GPT,designers can use natural language descriptions as input to generatecorresponding interactive prototypes,avoiding the tedious

123、 manual construction andimproving the quality and accuracy of prototypes.Additionally,GPT not only assistsdevelopers in completing routine code writing but also achieves intelligentprogramming through ML and NLP technologies.By understanding developersintentions,it generates code based on natural la

124、nguage descriptions,therebyimplementing more advanced and complex functionalities.Dragana Krstic et al.12proposed a ChatGPT-based framework for channelcapacity calculation in mobile networks to automate the simulation process in radioaccess network(RAN)planning,as shown in Figure 2-2.In this framewo

125、rk,ChatGPTemploys a model-driven approach based on dialogue agents and the Neo4j graph24/92database,assisting in multiple steps such as automated data import,graphconstruction,and ML-related queries.Neo4j is a high-performance and scalable graphdatabase management system,focusing on storing and proc

126、essing graph-structureddata.The results show that the quality of service(QoS)estimation method based onChatGPT is superior in accuracy and training speed compared to solutions based ondeep neural networks.Moreover,compared with the traditional simulation processbased on manually generated code,using

127、 ChatGPT to automatically generate code canshorten the simulation time.Figure 2-2 GPT Helps Conduct Channel Capacity Analysis Experiments122.1.3Enhanced Semantic CommunicationWith the development of new network technologies such as the Sixth Generation(6G)mobile communication system and the Internet

128、 of Things(IoT),the trend ofintelligent Internet of Everything has emerged.Semantic communication is expectedto become the core paradigm of future communication networks.However,existingsemantic communication is limited by the lack of contextual reasoning ability andbackground knowledge.Additionally

129、,training semantic models and constructingsemantic knowledge graphs consume significant time and computational resources.Therefore,improving the efficiency of model training,reducing the training costs,andachieving efficient transmission and deployment of models in networks are essentialfoundations

130、for semantic communication and are critical challenges faced by theindustry.TheintroductionofGPT-relatedtechnologiesenablessemanticunderstanding and representation learning of inputs and performs semantic matchingtasks.The literature13proposes a new AI-assisted SemCom network framework.Byadopting gl

131、obal and local GPT models,in the GPT-based enhanced semanticcommunication system,the transmitter and receiver respectively deploy semantic25/92encoding modules and decoding modules.The semantic models corresponding to themodules are respectively used to extract and recover semantic information.Seman

132、ticmodels are generated based on GPT on the server,and adaptive semantic models aredynamically deployed basedon requests from the transmitter and receiver.Simultaneously,the transmitter and receiver store semantic models in their respectivesemantic model libraries.The transmitter inputs the original

133、 information into thesemantic extraction and representation module to obtain semantic information.Thesemantic information is then converted into bit data through joint semantic encodingand channel encoding and transmitted.The receiver performs joint channel decoding,semantic decoding,and semantic in

134、formation recovery to reconstruct the originalinformation.Factors such as context and communication environment affect therecovery of semantic information,and the semantic decoding module compensates forerrors caused by background factors.This method achieves multimodal semanticcontent understanding

135、 and semantic-level joint source-channel encoding,improvingthe reliability and resource utilization of semantic reasoning to some extent,reducingtransmission traffic,lowering latency,and achieving more effective semantic delivery,as shown in Figure 2-3.Figure 2-3 Enhanced Semantic Communication2.1.4

136、Reshaping the Field of Chip DesignIn the field of communication,chip design plays a crucial role and can be26/92considered a key driving force for the development of communication technology.The chip design can support various communication protocols,such as Ethernet,wireless communication,Bluetooth

137、,and Long-Term Evolution(LTE),to facilitatecommunication and data transfer between devices.The chip design can providededicated hardware accelerators,codecs,and signal processors to support efficientmultimedia data processing and transmission.Wireless communication chips havefunctions such as RF fro

138、ntend,modems,and power amplifiers,which are used forwireless signal transmission and modulation/demodulation.The chip design canprovide hardware security features for data encryption,identity authentication,securecommunication,etc.,to protect communication systems from malicious attacks anddata leak

139、age.GPT can greatly improve the efficiency of chip design,shorten the design cycle,and automate the design process further,thereby enhancing the efficiency and qualityof design.GPT-4 can lower the threshold for chip design,enabling more people toparticipate in chip design,which may lead to more inno

140、vations.In September 2023,researchers from the Tandon School of Engineering at NewYork University14successfully designed a chip using OpenAIs GPT-4 model,asshown in Figure 2-4.This marks a significant breakthrough for AI in the field ofhardware design.GPT-4 generated viable Hardware Description Lang

141、uage(HDL)code through simple English dialogue,and the benchmarking tests and processorswere successfully taped out on the Skywater 130 nm shuttle.The application of GPT-4 in chip design represents a major breakthrough for AI in the field of hardwaredesign.We have reasons to believe that AI will play

142、 a greater role in the future of chipdesign,bringing more efficient and innovative chip design solutions.Figure 2-4 Application of GPT in Chip Design2.2.GPT Promotes IntelligentAutonomy in Communication NetworksAI-enabled autonomous networks are an important trend in the development ofthe Fifth Gene

143、ration(5G)mobile communication system and subsequent 5G networks,27/92bringing fundamental changes to mobile networks.Networks will gradually transitionfrom the current passive management mode primarily driven by human interventionto a self-driven autonomous management mode.In the future,intelligent

144、 networkswill provide more flexible and efficient network policies through multidimensionaldata sensing,including service data,user data,and network state data,based on AI-drivenintelligenceanalysis.Thiswillenablehighlyautonomousnetworks,significantly improve the efficiency of the entire lifecycle o

145、f mobile networks,andreduce network operation costs.The core concept of intelligent autonomous networks15is to promote thedevelopmentofthenext-generationcommunicationnetworkstowardsself-configuration,self-healing,self-optimization,and self-evolution by introducingtechnologies such as AI.“AI+communic

146、ation”has become one of the six majorscenarios defined by the ITU for 6G16,including assisting autonomous driving,autonomous cooperation between devices,assisting medical applications,and eventprediction based on digital twins.Currently,AI has been preliminarily applied in thefield of network intell

147、igent autonomy.Many operators,equipment vendors,and third-party manufacturers worldwide have begun studying network intelligent autonomy,asshown in Figure 2-5,including applications such as network planning,slicedeployment,network O&M,and network optimization.28/92Figure 2-5 GPT Promotes Intelligent

148、 Autonomy in Communication Networks2.2.1GPT Reshapes Network PlanningDue to the expected exponential growth in the number of wireless access devicesin the coming years,operators need to expand the deployment scale of networkinfrastructure to provide the required capacity.Traditionally,the selection

149、of new basestation sites is manually conducted by wireless network planning professionals.Withthe assistance of coverage simulation tools,each site is evaluated based on KeyPerformance Indicators(KPIs),and ranked accordingly.However,when the numberof available site options is large,the traditional a

150、pproach becomes costly andchallenging to accurately consider the impact of each involved factor.AI-drivenplanning solutions can recommend the best locations for new cellular base stations,helping operators reduce network planning costs.To address the optimal site selection problem,Siddhartha Shakya

151、et al.17proposedan AI-based site selection method.Based on this,they conduct network site selection29/92planning by using GPT.Historical spatiotemporal feature data is collected to analyzethe variation patterns of wireless resource utilization rates,and monitor and evaluatethe KPIs of coverage cells

152、.GPT is used to comprehensively analyze networkcoverage,userequipmentdistribution,andscenariocharacteristics.Also,unsupervised ML is employed to cluster cells based on their attributes.A supervisedregression model captures the relationship between different cell attributes,such ascell performance an

153、d user equipment throughput.A simulation algorithm is built onthe regression model to estimate the potential traffic load of proposed new sites.Sitesare ranked based on a scoring mechanism,and thereby selecting top-ranked sites ascandidate base stations.In addition to site selection,antenna design i

154、s also an important task in the basestation planning phase.In the optimization design process of antennas,a large numberof antenna parameters are typically involved,the geometric shapes of antennas arebecomingincreasinglycomplex,andconflictsbetweenantennaperformancerequirements frequently arise.Intr

155、oducing GPT into antenna simulation design canreplace the role of electromagnetic simulation software,adjust antenna parametersbased on simulated application scenarios,and utilize particle swarm intelligenceoptimization algorithms18for rapid antenna simulation and optimization design.Compared to ele

156、ctromagnetic simulation software,this approach can further enhancecomputational efficiency.2.2.2GPT Enhances Slicing DeploymentThe introduction of network slicing has successfully solved the problem ofuneven allocation of network resources for different service scenarios,bringing greatflexibility to

157、 the network,making it possible to customize and deploy in real time,anddynamically guarantee the network.During network slicing deployment,slices fordifferent service scenarios have different resource requirements on the underlyingphysical network,and there are differences in deployment structures.

158、Traditionalalgorithms struggled to solve the multi-scenario slice deployment problem.By usingGPT-related technologies,it is possible to achieve end-to-end network slicingdeployment while reducing deployment costs and ensuring stronger security.Network slicing deployment involves the placement of Vir

159、tualized NetworkFunctions(VNFs)and the selection of related links.VNF placement refers to ensuring30/92that nodes in network slicing requests can always find corresponding nodes on thephysical network to accommodate the requests,under the condition of meetingnetwork capacity.Compared with the tradit

160、ional heuristic algorithm to solve the VNFmapping process,GPT can analyze the network environment status,intelligentlyadjust network parameters according to service scenario requirements,and makeservice resource demand predictions.By interacting with the agent and theenvironment,specific actions are

161、 executed,the network resource utilization is updated,and the state information in the VNF mapping process is fully perceived.Timelydecisions can be made in response to changing network conditions.As shown inFigure 2-6,GPT assists in obtaining network deployment environments and storesphysical node

162、information as a security feature matrix.An agent is defined as a policynetwork relying on GPT to calculate the probability of physical node mapping.GPTassists in computing the probability of physical node mapping outputting the securityfeature matrix,and then selects the physical node with the high

163、est probability andperforms VNF mapping.Subsequently,GPT selects the most suitable link mappingscheme based on different service requirements,uses network resource utilization as areward function,provides feedback to the agent,and updates state informationsimultaneously.Figure 2-6 GPT Enhances Slici

164、ng Deployment2.2.3GPT Simplifies Network Operations and MaintenanceTypical application scenarios of network intelligent O&M include anomalydetection,fault diagnosis,event warning,and performance optimization.In traditionalnetwork O&M,O&M personnel need to obtain network state information throughmanu

165、al inspection and data analysis,which is inefficient.By introducing GPT-related31/92technologies,network state information can be monitored in real-time and efficiently,and the network can be analyzed and processed through automated O&M,therebyeffectively improving the stability and reliability of t

166、he network.As shown in Figure 2-7,the network collector sends real-time networkinformation to GPT,such as the devices Central Processing Unit(CPU),memory,network congestion information,and network event log information.GPT rapidlyconducts statistical analysis,and then combines various network servic

167、e scenarios topredict the network andprovide corresponding O&M decisions,which aresubsequently sent to the database for storage.In addition,GPT assists in thevisualization of the current and predicted network conditions,better showing thenetwork status and trends to O&M personnel,and assisting O&M p

168、ersonnel toconduct network O&M more efficiently and intelligently.Figure 2-7 GPT Simplifies Network O&MFurthermore,in conjunction with GPT-related technologies,the literature19hascomprehensivelyanalyzedtheunderlyingcomponentsusingintent-driven,communication-network-specific ML models and advanced po

169、licies.This approachachievesanautonomousloopof“faultlocalization-policygeneration-policyverification.”Unlike traditional intelligent O&M methods assisted by manualdecision-making,this method uses spatiotemporal representation learning forknowledge inference in network operation state detection,autom

170、atically generates andverifies fault recovery and multitask management policies,and ensures servicebandwidth and network performance through techniques such as bypass routing andresource orchestration.Moreover,it can autonomously perform fault repairs based onlearning results to support network mana

171、gement and state adjustments,thereby32/92promoting autonomous network O&M.2.2.4GPTAccelerates Network OptimizationWith the rapid development of the socio-economy,there is an increasing demandfor access methods of information and content.The service requirements of mobilecommunication are constantly

172、changing and evolving from the initial demand formedium to low-rate voice services to the demand for broadband high-rate dataservices,further transforming into meeting the needs of massive differentiatedservices.Network optimization refers to the process of discovering and solvingproblems through a

173、series of professional tests and analyses targeted at mobilecommunication systems,while also deeply developingsystem potential andenhancing system performance.Its main targets include the core networks of dataservices,circuit-switched core networks,radio access networks,and so forth.Ascommunication

174、network processes and services become more diverse,the pressure onnetwork operations increases,and the introduction of AI will be the key to solvingnetwork optimization problems.Currently,GPT-based network optimization canachieve self-correction and optimization of the network through autonomousdete

175、ction,analysis,and operation,mainly including network traffic optimization,radio access network coverage optimization,and network signaling tracking,asshown in Figure 2-8.1)Network traffic optimizationWith the continuous change in user service demands,traffic also dynamicallychanges.GPT can extract

176、features based on traffic changes,predict their trends,provide optimization solutions to balance network loads and ensure a satisfactory usernetwork experience.It predicts and configures scheduling for areas and times withhigh traffic in advance,and intelligently shuts down part of the base station

177、facilitiesin cells and times with low traffic,thus achieving cost savings and ensuring that thecommunication network operates at its optimal level.2)Radio access network coverage optimizationThe extent of radio access network coverage determines the quality of thecommunication network.Statistics sho

178、w that the total sum of wireless parameters foreach equipment vendor in LTE networks has exceeded 8000,making it difficult toconduct refined configuration based on manual experience alone.Some scholars haveproposed using AI technology to conduct a systematic analysis of communication33/92networks,en

179、ablingprecisenetworkparameterconfiguration20.GPT-relatedtechnologies can be introduced on this basis.For example,when facing coverageproblems with the TopN cells,a Graph Neural Network(GNN)model trained by GPTcan be utilized to construct a regional coverage model.This model inputs featureinformation

180、 affecting coverage,such as base station structure and parameterconfiguration data.Then,through hidden layers,the model is trained and features arelearned.When the algorithm iterates to a certain degree,it can represent the coverageprediction model,recommend parameter values,and guide the adjustment

181、 andconfiguration of wireless parameters through high-level features.Additionally,it canassist operators in incorporating the beam coverage and transmission modes ofmassive Multi-Input Multi-Output(mMIMO)antennas into consideration,resolvingvarious signal coverage differences caused by the different

182、 heights of target usersindoors and outdoors.Fully utilizing the 3D characteristics of mMIMO antennas andenvironmental features can ensure accurate site planning,thereby achieving betterradio access network coverage optimization.3)Network signaling trackingSignaling information refers to control com

183、mands in communication systems,also known as“signaling,”mainly used for processing and controlling communicationprocesses.By processing signaling information,communication processes can bemonitored,managed,and optimized to improve the quality and efficiency ofcommunication.AI signaling tracking meas

184、ures have been proposed in theliterature21,and with the introduction of GPT-related technologies,it can monitor andanalyze a large amount of signaling information to discover potential faults orabnormal situations,thereby understanding the actual operation status of the network.Additionally,GPT can

185、quickly locate faults by comparing normal and abnormalsignaling flows,provide fault diagnosis reports,and achieve automatic fault repair,thereby reducing network downtime and O&M costs.Furthermore,based on real-timesignaling information and user demands,GPT-based signaling tracking can predictnetwor

186、k traffic demand,dynamically manage,allocate,and schedule resources toadapt to changes in time,region,and user type.At the same time,it can also analyzeand monitor signaling information and latency during the user connection process,promptly identify and resolve issues affecting user experience,such

187、 as high latencyand weak signals,to improve user satisfaction and ensure a stable and efficientnetwork environment.34/92Figure 2-8 GPTAccelerates Network Optimization35/923.Communication Networks Enable GPT Ubiquitous ApplicationsIn Chapter 2,we introduced the novel applications of GPT in the field

188、ofcommunication,as well as how GPT promotes the intelligent autonomy ofcommunication networks.GPT will be applied in more and more scenarios,and howto support and optimize GPT in the field of communication is the next step we need toconsider.When designing future network architectures,the potential

189、demands forGPT deployment should be included in the planning scope to enable ubiquitousapplications of GPT,further meeting the diverse demands of users.Additionally,therapid development of edge intelligence technology in recent years has provided abroader application space for GPT.Edge intelligence

190、with GPTs generalizationabilities has tremendous potential.Research on how to facilitate the convenientdeployment of GPT and how to“edge”GPT from the“cloud center”will requiremore exploration in future network design.In this chapter,we will introduce how existing communication networksguarantee the

191、landing of GPT applications,discuss typical ideas for future networkdesign and explore how to achieve native support for GPT.Based on this,keytechnologies supporting massive data training and inference acceleration in newnetwork architectures are studied to achieve endogenous intelligence.3.1 Commun

192、ication Networks Guarantee the Landing of GPTApplicationsIn recent years,with the rapid development of cloud computing,big data,AI,andthe continuous maturity of various application scenarios,more and more data needs tobe uploaded to the cloud for processing,which has brought a heavier workload toclo

193、ud computing22.The emergence of edge intelligence has reduced reliance on thecloud,providing edge intelligence services for industry digitization,and meetingcritical requirements such as agile connection,real-time service,data optimization,application intelligence,security,and privacy protection23.I

194、n addition,AIGCeffectively addresses the limitations of high latency and high risk in cloud-edgenetworks,making data more secure and reliable,functions more stable,and servicesmore efficient and intelligent.Figure 3-1 illustrates edge intelligence supported bymobileAIGC networks.36/92Figure 3-1 Mobi

195、le AIGC Network Supports Edge IntelligenceWith the continuous evolution of edge intelligence,integrating advanced NLPtechnologies such as GPT into edge intelligence systems can provide them withstronger language understanding and generation capabilities.Edge intelligenceprovides an open platform tha

196、t integrates network,computing,storage,andapplication core capabilities for the deployment of GPT,and edge intelligence withGPTs generalization abilities has a wider range of application scenarios.In the medical field,to address medical disputes,improve the legal constructionof medical institutions,

197、and provide diagnosis and consultation services forindividuals,Guangzhou Medical University,three operators,and Fontdo Technologiesjointly launched a GPT vertical large model 5G messaging application for medicalcompliance and legal knowledge in diagnosis and treatment.This application,basedon networ

198、k message traffic,serves as an entry for GPT,providing a platform for userinteraction and supporting GPT technology to deliver intelligent question-and-answerservices and personalized recommendations,thereby enhancing the QoS.The novelapplication,validated through the commercial mode of communicatio

199、n network37/92messaging,can find broad adoption in the industry.By providing professionalknowledge in the medical field,the research field of GPT large models is narroweddown to the subdivision field of medical disputes,making the service more targeted.It can answer medical practitioners questions a

200、bout medical-related laws andregulations and provide relevant regulations and standards.It can also provideconsulting and claims services for patients.It is reported that since the launch of theproject,there have been over 100,000 consultations,with an average of over 10.3rounds per conversation.Thi

201、s application provides doctors with multiple entries formedical legal scenarios,effectively improving their legal awareness and quicklyobtaining solutions.As a consultation entry for patients,it can save legal resources atthe level of medical disputes.In addition,the application can also provideprof

202、essional services to effectively avoid lengthy and costly medical dispute lawsuits,reducing the waste of economic resources.In terms of intelligent office,Unisound,in collaboration with Longhua Data Co.,Ltd.in Shenzhen,based on the Shanhai large model,launched the“Longzhizheng”government GPT large m

203、odel tailored for vertical fields in the industry,becoming alanding practice project focusing on specific fields for GPT.The model has sevengeneral capabilities:language generation,language understanding,knowledge Q&A,logical reasoning,code,mathematics,and safety compliance,as well as three industry

204、landingcapabilities:plug-inextension,domainenhancement,andenterprisecustomization,which can meet different requirements in different scenarios.“Longzhizheng”is based on the GPT large model and is applied to the DistrictGovernment Services Data Management Bureau.Through high-quality governmentlanguag

205、e training and parameter fine-tuning,it has accurate knowledge servicecapabilities in government affairs fields.Unlike traditional information systems,the“Longzhizheng”GPT large model provides GPT with exclusive data security isolation,multi-round dialogues,and information traceability.It seamlessly

206、 integrates all policydocuments and professional terminologies from the professional knowledge base,comprehending the semantic and contextual needs of enterprise masses,therebydisrupting the traditional online machine Q&A mode and transitioning informationservice from manual search to proactive,bidi

207、rectional,and real-time intelligentguidance.The GPT large model can be trained based on existing policy documents,normative documents,government documents,and other materials,learning variouswriting styles,establishing writing models,mastering service essentials,and writing38/92norms,aiding governme

208、nt personnel in achieving more accurate and efficient taskssuch as summarization,document drafting,and text retrieval,which previouslydemanded substantial efforts.3.2 Future Network Technology Supports GPTApplicationsOver the past 20 years,communication networks have evolved from connectingpeople to

209、 connecting things.Looking towards 2030 and beyond,human society willenter a new era of intelligence.In this era,social services will become more equitableand efficient,social governance will be more scientific and precise,and socialdevelopment will be more environmentally friendly and energy-effici

210、ent.Futurenetwork technology will be a crucial enabler in achieving these goals,transitioningfrom serving people and things to serving intelligent entities,and efficientlyconnecting with GPT.Future network technology will further support GPTapplications by enabling intelligent interconnections among

211、 humans,machines,andobjects,facilitating intelligent production and living,ensuring high-quality economicand social development,and promoting the construction of a universally intelligenthuman society.3.2.1 Typical Approaches to Future Network DesignDesigning future networks involves various fields

212、and technologies,such as theInternet of Things,cloud computing,AI,blockchain,and network security.Differentapproaches and solutions are required for different application scenarios and demands.Thedesignphilosophyshouldprioritizecompatibility,cross-domaindesign,distributed design,simplicity,security,

213、and endogenous design,fostering incrementalinnovation and ensuring the integrated access of various new capabilities.This makesthe network architecture more flexible,concise,and secure.Additionally,activeincorporation ofAI technology is necessary24.6G networks will continuously empower society throu

214、gh autonomous learningand device collaboration,ensuring that AI services and applications like GPT areavailable to every end-user,making real-time and reliableAI intelligence accessible toevery individual,family,and industry,and achieving genuine universal intelligence25.The new network architecture

215、 needs to flexibly adapt to tasks such as collaborativesensing and distributed learning to facilitate the widespread adoption of GPTapplications.In the architectural design of future networks for GPT technology,native39/92support for GPT should be ensured from the outset of network design,rather tha

216、ntreating GPT solely as an optimization tool.Figure 3-2 Universal Intelligent NetworkIn addition to native GPT application support,future networks need toincorporate new features such as native data protection,native trustworthiness,andnative multi-ecosystem,as shown in Figure 3-2.The concept of“tru

217、stworthiness”encompassesvariousaspects,includingnetworksecurity,privacy,resilience,functional safety,and reliability26,necessitating future network designs to prioritizenetwork security and privacy protection27and implement multi-level and all-roundsecurity protection measures.Future networks should

218、 natively support various typesof network access,forming a diverse ecosystem to facilitate universal intelligence.What helps universal intelligence is that AI technology will be endogenous to thefuture mobile communication system and present a new intelligent networktechnology system through wireles

219、s architecture,wireless data,wireless algorithm,and wireless application.3.2.2 6G Network with Native Support for GPTApplicationsIn the realm of 6G,AI capabilities are no longer supplementary features butinherent characteristics.A primary goal of 6G is to achieve ubiquitous AI and enableubiquitous G

220、PT applications.In the era of 6G,network architecture and GPT will be40/92closely integrated,with 6G providing end-to-end support for GPT-related services andapplications.However,a 6G network that natively supports GPT represents ouroptimistic vision for future networks and still needs to meet the f

221、ollowing diverserequirements.1)Robust mobile edge computing supportFuture networks need to offer robust mobile edge computing support withcomputing power exceeding 100 G FLOPS.This enables the deployment andoperation of GPT models on edge devices closer to users,enhancing response timesand reducing

222、the load on core networks.2)Real-time data exchange and processing28GPT models engage in extensive data exchange and processing,requiringnetworks to provide low-latency and high-bandwidth support.During transmission,its crucial to ensure rapid and stable data transfer from source to destination,achi

223、eving speeds of over 100 Gbit/s and reducing latency to below the second level tomeet GPT modelsreal-time data transmission needs.3)Strict security and privacy protectionThe data processed by GPT models sometimes involves companies commercialsecrets and users private information.Hence,networks need

224、to provide robustsecuritymechanisms,includingend-to-endencryptedtransmission,identityauthentication,and access control,to safeguard data security and privacy.4)Intelligent network management and optimizationFuture networks require intelligent management and optimization capabilities todynamically ad

225、just network resource allocation and path selection,and also optimizenetwork topology based on GPT models real-time demands,ensuring optimalnetwork support for GPT models.5)Diverse network access needsConsidering that GPT models may be accessed and utilized through differenttypes of devices(such as

226、mobile devices,and IoT devices),future networks need tosupport diverse connectivity needs,including wireless access,mobile access,and low-power connectivity,to meet GPT modelsusage in various scenarios.The 6G network architecture natively supporting GPT fully utilizes thecommunication,computing,and

227、sensing capabilities of network nodes.Throughdistributed learning,swarm intelligence collaboration,and cloud-edge-terminalintegrated deployment,it constructs a new AI application ecosystem and user-centric41/92service experiences.Moreover,distributed AI running at the network edge canapproach peak p

228、erformance and address concerns regarding data ownership,whichare crucial for both individuals and enterprises.The integration of universalintelligence with ICT systems,providing diverse connectivity,computing,and storageresources at the network edge,will become an inherent feature of 6G.It is fores

229、eeablethat a 6G network architecture offering native GPT support will transition from thecurrent centralized“cloudAI”model to a distributed“network AI”model29.3.3 New NetworkArchitecture Supports GPT Capability SinkingAs 6G networks rapidly evolve,we are entering a new era centered around data.Tradi

230、tional network architectures,focused on connectivity,are transitioning to moreintelligent,efficient,and data-centric network information systems.This evolution notonly involves reforming network infrastructure but also includes effectively managingand utilizing the ever-growing data to support a wid

231、e range of intelligent applicationsand services like GPT.Existing network architectures suffer from limitations such aslack of support for data processing and intelligent decision-making,high latency,insufficient bandwidth,centralized computing power,and low network adaptability.To support GPT capab

232、ility sinking,networks need efficient and flexible datatransmission capabilities and powerful distributed data processing capabilities.Starting from the network deployment design,it is necessary to reduce the networksdependence on central computing,ensuring data accuracy while reducing unnecessaryda

233、ta backhaul.3.3.1Adaptive SlicingIn the 6G era,mobile network services are no longer limited to phones butencompass various types of devices,such as sensors and vehicles,with increasinglydiverse and rich service types.Building specific networks for each typical service tomeet its unique requirements

234、 would significantly constrain business development dueto high network costs.Furthermore,if different services are all carried on the sameinfrastructure and network elements,the network may not simultaneously meet thediverse QoS guarantee requirements of multiple services.Adaptive network slicing te

235、chnology allows the creation of multiple virtual42/92network slices,each being a set of network functions and their resources.Thesenetwork functions form a complete logical network,with each logical networkcapableofmeetingspecificservicerequirementswithparticularnetworkcharacteristics.Through custom

236、ized network functions and protocols,network slicingprovides matched network capabilities for different service scenarios.Each slice canindependently tailor network functions and manage corresponding network resourcesbased on the real-time network conditions and service requirements,exemplifying the

237、instantiation of 6G network architecture.This flexibility enables 6G networks tosupport multiple services and applications with specific performance requirementssimultaneously,suchaslatency-sensitiveapplicationsandhigh-bandwidthapplications.The key is to dynamically adjust resource allocation and co

238、nfigurationbased on real-time network conditions and service demands.When facing high-performance computing requirements like those of GPT,adaptive network slicingdemonstrates its unique advantages.As shown in Figure 3-3,network slicing,as aservice delivery method,can be applied across various verti

239、cal industries,providingnetwork capabilities as per application scenarios and service types,with slices isolatedfrom each other and non-interfering.Figure 3-3 Hierarchical Network Slicing Management43/923.3.2 Distributed LearningDistributed learning will become a vital avenue for enhancing AI perfor

240、manceand efficiency in 6G networks.Distributed learning can expand the sample space,deploy larger models,design global optimization algorithms,and improve theefficiency of network AI development and training.In terms of intelligent services,distributed learning enables interaction among intelligent

241、nodes to share applicationlearning experiences.In network intelligence,it has significant application value inthe most common distributed intelligent decision-making,including control decisionsof general mobile intelligent agents,intelligent recommendation decisions of variousdistributed facilities,

242、event judgment decisions of environmental monitoring,andoptimization configuration of network parameters.In the future,intelligent networkswill form a new pattern in which traditional centralized training supports globalintelligent applications,and distributed learning supports the autonomous intell

243、igenceof each distributed node.Compared to the traditional operation mode of relatively isolated transmissionand computing from end to cloud,the most significant feature of using distributedlearning is the fusion of distributed network computing.By involving distributedcomputable nodes in the networ

244、k,redundant data transmission is reduced,therebylowering system communication costs and enhancing computing efficiency.Federatedlearning is a typical system of communication-computation and network-intelligenceinteraction.In the traditional federated learning framework,multiple distributed nodestrai

245、n with local data and periodically upload models.After integrating and updatingmodel parameters centrally,the updated models are distributed back to each node.Communication supports data transmission and interaction between nodes,while thecomputing process affects system scheduling and model accurac

246、y.Communicationand computing are coupled,jointly determining the systems reliability and efficiency.3.3.3 Edge IntelligenceAs shown in Figure 3-4,edge intelligence can be traced back to the developmentof distributed computing30.By decomposing computing tasks into multiple subtasks,LLM training can b

247、e processed more efficiently,achieving significant advancement ingeneralization ability.The further evolution of edge intelligence involves deeplyintegrating sensing,computing,and decision-making into one.The key goal of the44/92sensing phase is to collect high-quality data,providing sufficient info

248、rmation forsubsequent computing and decision-making.With the rapid development of deeplearning and neural network technologies,the role of computing in edge intelligencehas become increasingly important.AI models on edge devices can parse andunderstand user language,making corresponding decisions,an

249、d enabling more naturaland intelligent interaction between users and devices.The fusion of sensing,computing,and decision-making improves the intelligence level of the system31.Byrealizing intelligent analysis and decision-making on edge devices,the system canrespond to different tasks and environme

250、ntal changes more quickly and flexibly,providing users with more personalized and intelligent services.Figure 3-4 Edge Intelligence Concept EvolutionThe evolution of edge intelligence not only involves the expansion of its conceptbut also reflects the continuous enrichment of its key features.When e

251、quipped withGPTs generalization ability,these features make edge intelligence more appealing invarious industries and application scenarios:Adaptability32:Automatically adjust network behavior based on environmentaland task changes and continuously accumulate experience and data through GPTslearning

252、 capabilities to improve prediction accuracy and network performance.45/92Multimodal fusion33:Effectively integrate and process various types of data,including images,audio,and videos from multiple sensors to provide morecomprehensive intelligent analysis and decision-making.Low power consumption34:

253、Through optimization algorithms,hardware design,and power management policies,edge intelligence systems can maintain highperformance while minimizing energy consumption,thereby extending the lifespan ofdevices.Mobile edge computing and cloud collaboration34:Edge devices handle real-timelocal tasks,w

254、hile cloud servers manage more complex and computationally intensivetasks,with both collaborating to achieve optimal performance and resource utilization.Real-time performance34:By executing intelligent analysis and decision-makingwith GPT on edge devices,the system responds more quickly to user dem

255、ands,reducing communication latency and enhancing user experience.Security and privacy protection33:With data processed locally,edge intelligencehelps reduce security risks associated with data transmission and protects user privacythrough encryption,authentication,etc.46/924.Collaborative Developme

256、nt of GPT and CommunicationMobile communication technology has evolved at a pace of one generation perdecade,enriching the ways people communicate and driving changes in social life andeven production modes.With the rapid rise of large models and generative AI,theapplication of AI technology has ent

257、ered a new stage.As a typical representative ofthe new generation of AI,GPT is becoming increasingly linked with communication,progressing from independent evolution and fusion development to collaborativeevolution,empowering various fields of production and daily life.As society and theeconomy deve

258、lop,peoples demands become more diverse.6G networks are expectedto introduce new application scenarios and key performance indicators.This chapterwill discuss how to utilize GPT-related technologies to promote in-depth research on6G new networks.It will also explore how to integrate 6G with GPT to s

259、upport digitaltransformation in more industries,achieving greater social and economic value.4.1.GPT and Communication from Independent Evolution to Close Integration4.1.1Trends in the Integration of GPT and CommunicationAs early as 2008,3GPP defined Self-Organizing Networks(SON)based on Rel8,embeddi

260、ng ML and AI functionalities into the lifecycle of network planning,analysisanddesign,implementationandconstruction,andoperationandmaintenance33343536,marking a milestone in the development of communicationAI.However,2G and 3G networks were not initially constructed based on the conceptof network in

261、telligence,making it difficult for the ecological system of the oldnetwork era to adapt toAI.In September 2017,3GPP defined the Network Data Analytic Function(NWDAF)as the first network element for communication AI.Additionally,O-RANdefined the Radio Access Network Intelligent Controller(RIC)as anot

262、her networkelement for communication AI,analogous to the communication AI brain within thenetwork.In June 2018,the 3GPP 5G New Radio(NR)standard Stand Alone(SA)solutionwas officially completed and released at the 80th3GPP TSG RAN plenary meeting.Compared to 4G networks,5G networks have made qualitat

263、ive leaps in keyperformanceindicatorssuchastransmissionrate,transmissionlatency,and47/92connection scale,supporting richer service scenarios and applications,thus creatingconditions for the applications ofAI tools represented by GPT.However,5G networksface challenges during operation,including netwo

264、rk complexity,high energyconsumption,and poor control flexibility,which bring uncertainties373839.In March 2023,ETSI proposed standards and specifications regarding AItransparency and interpretability,aiming to generate more interpretable models whilemaintaining a high level of model performance.In

265、September 2023,the 3GPP AI/ML working group introduced generative AIinto the discussion scope and incorporated it into the NWDAF module.After severaliterations,a distributed network big data analysis architecture supporting datacollection,training,inference,closed-loop control,and diverse solutions

266、has beenestablished.The relevant network functions and interface specifications have maturedand have the ability to accelerate industrialization.In December 2023,3GPP Release19 focused on new scenarios and technologies to make AI understand networks better,achieving deep integration of networks and

267、AI,including application directionscombining AI with 5G in the future.AI,represented by GPT,has the ability to solvecomplex and unstructured network problems by analyzing data collected from thenetwork.For specific use cases,3GPP has studied how to apply AI to 5G RAN andthe physical layer.In Februar

268、y 2024,ETSI explored the applications ofAI in different fields such ashealthcare,smart transportation,and industrial automation,as well as AI-relatedfunctions in future mobile networks.This provides new ideas for further applicationsof GPT in the communication field.The integration of GPT and the co

269、mmunicationindustry is evolving from independent evolution and cutting-edge intersection tofuture collaborative evolution and close integration,which is a complementary resultand an inevitable trend in development,accelerating the mutual progress of both.4.1.2Integration of GPT and 5G NetworksThe in

270、tegration of GPT with the physical layer contributes to achieving flexibledesign in 5G.Massive MIMO is the cornerstone of 5G NR design.NR needs tosupport a spectrum range of up to 100 GHz,and as the frequency increases,thenumber of antennas used in the transceiver system also increases accordingly.G

271、PTcan comprehensively consider the beam coverage and transmission characteristics ofantennas40,fully utilizing the environmental characteristics around the antennas to48/92configure and optimize antenna-related parameters,which is the direction that canfully leverage the powerful reasoning ability o

272、f GPT.The integration of GPT with the network layer contributes to achieving flexibleautonomy in 5G.Massive data can be obtained through the 5G network,andstructured data accounts for a high proportion.GPT performs statistical analysis onlarge datasets41,and the results can be used for more refined

273、scheduling andoptimization of the entire network,improving network resource utilization.The integration of GPT with the application layer can provide end-to-enddeterminism.By introducing GPT,service requirements are transformed intoindicators perceivable by the network,and the indicators are analyze

274、d and predictedto determine whether the current network resources meet the service requirements.Furthermore,combined with the trends of indicators obtained from analysis andprediction,it helps in dynamic planning,scheduling,and optimization of networkresources,laying a solid foundation for providing

275、 high-reliability service assurance inthe end.4.2.Integration and Development of GPT with 6G Communication NetworksThe integration of 6G networks with GPT is a potential development direction inthe future.It includes both the intelligent capabilities provided for the performanceoptimization of 6G ne

276、tworks themselves,such as using end-to-end AI to achievecustomized optimization and automated O&M of air interfaces and networks,andproviding the best solutions to meet diversified needs;the intelligent capabilitiesprovided to third-party services,such as accelerating the evolution of centralizedclo

277、ud intelligence to ubiquitous edge intelligence through the native integration ofcommunication,computing and sensing capabilities of 6G network elements,providing a distributed learning infrastructure for GPT serving third-party services.“6G+GPT”services primarily address requirements for high real-

278、timeperformance,high efficiency,and strong security,conducting training or inferencewithin the network to deliverAI capabilities tailored to different application scenarios.As a native intelligent architecture,6G networks can use the resources ofcommunication,computing,data sets and foundational mod

279、els in the network,combined with the efficient training or inference capabilities of GPT,to realize taskssuch as massive data processing,network self-service,resource optimization,andendogenous security,providing users with ubiquitous high-performance AI services.49/92The following section elaborate

280、s on four specific directions for the integration anddevelopment of 6G with GPT,as shown in Figure 4-1.Figure 4-1 Tight Integration of GPT with 6G4.2.1GPT Supports Massive Data Processing6G networks require services for basic data operations such as massive datacollection,preprocessing,distributed s

281、torage,and high-speed transmission.Theamount of data is exploding in the information age,and massive data resourcescontain huge value.With the increase of more advanced intelligent terminals andwireless edge devices in the 6G era,the requirements for edge computing power willbe further improved.NVID

282、IA has already released dedicated GPUs for GPT,whichcan boost inference speeds by up to 10 times,meeting the high computingrequirements of 6G.Additionally,based on the multimodal capabilities of GPT,whichcan simultaneously handle various types of data such as text,images,and audio,powerful data proc

283、essing and analysis capabilities help further unify the standards ofdata services and applications(including data formats,parameter definitions,andcalculation methods)within the 6G network.This facilitates the rapid circulation andshared application of data resources within the 6G network,enabling i

284、ntelligentcomputing centered on massive data.50/924.2.2GPT Promotes Network Self-ServiceOne of the characteristics of 6Gs endogenous intelligent network is its ability toadaptively match users personalized needs and provide network self-servicecapabilities.Specifically,as users demands for performan

285、ce metrics such asbandwidth,latency,computing power,and storage capacity dynamically change,thenetwork receives personalized demands from users.Based on GPTs analysis of userintentions and translation into QoS requirements for the network,further adjustmentsare made based on the perceived state of t

286、he current network,setting the QoSrequirements as the networks execution plan or policy,all without the intervention ofO&M personnel.4.2.3GPTAssists in Network Resource Orchestration6G networks are integrated information systems combining communication,sensing,and computing,requiring orchestration o

287、f communication and computingresources to meet user Service Level Agreement(SLA)requirements and achieveoptimalnetworkoperationefficiency.Orchestrationinvolvestheautomatedconfiguration,management,and coordination of computer systems,applications,andservices.After analyzing service requirements,GPT p

288、redicts resource consumptiontrends and optimizes communication and computing resource orchestration andscheduling schemes.Specifically,according to the analysis of service requirements,the network generates the optimization requirements of communication,sensing andcalculation,and sends them to the i

289、ntent management function.Using GPT assistance,comprehensive business intent sensing,network intent analysis,network capabilityconversion,specific business-aware SLA requirements are output and distributed,refining business SLA requirements into business transmission computing models andbase station

290、 resource consumption trends.Subsequently,learning algorithms are usedtooptimizenetworkconfigurationparametersthataffectsensingalgorithmperformance,generating network parameter optimization policies.Guided by resourceorchestration,GPT interacts with users to obtain feedback,thus iteratively optimizi

291、ngpolicies.4.2.4GPT Constructs Network Endogenous SecurityGPT is expected to help provide effective security techniques to defend againstand anticipate various network attacks,playing a crucial role in protecting 6G51/92networks from various security threats.Based on the zero-trust software-defineds

292、ecurity boundary,a new security boundary protection system has been applied to6G11.By continuously verifying the legitimacy and permissions of users,devices,and applications,it can define the security boundaries of objects completely throughsoftware in real time to determine whether each resource ac

293、cess is reasonablyauthorized.In the zero-trust framework,GPT conducts dynamic risk assessment basedon users,devices,and applications,and adjusts permissions in time;it constructsspecific risk control schemes based on user identity,behavior,time,address,application context,and other information.By in

294、tegrating comprehensive access riskrecords,GPTsynthesizeshistoricalexperiences,deeplyintegratingsecuritycapabilitieswithnetworkelementsandnetworkcharacteristics,andfurtherconstructing an endogenous security system for 6G.4.3.“6G+GPT”Empowers Industry Digital Transformation6G will possess native AI c

295、apabilities,enabling highly customized optimizationsin both air interface and network design through end-to-end AI and ML.Each networkelement will also inherently integrate communication,computing,and sensingcapabilities,transitioning from centralized intelligence to distributed networkubiquitous in

296、telligence.By leveraging the distributed ML architecture of edgeintelligence,6G will meet the large-scale intelligent production needs of society42.Asthe“super infrastructure”of the future digital world,6G will support ubiquitousintelligence for people,machines,and objects,empowering the entire soci

297、etys digitaltransformation and realizing the vision of an“Intelligent Connection of Everythingand Digital Twin.”After deep integration with 6G,GPT can be applied in a widerange of scenarios,supporting and providing numerous new services and applications.The most typical application areas include sma

298、rt home,smart healthcare,smartindustry,smart transportation,smart agriculture,and digital entertainment,amongothers,as shown in Figure 4-2.52/92Figure 4-2“6G+GPT”Empowers Industry Digital Transformation4.3.1“6G+GPT”Empowers Smart IndustryIn recent years,new technologies represented by AI,big data,an

299、d cloudcomputing have been rapidly integrating with traditional manufacturing,making“green”and“intelligent”manufacturing modes the key development direction of theindustry.The modern industrial intelligent production mode is built on the foundationof AI applications4344.The collaboration of 6G commu

300、nication technology and GPTcan fully leverage the strengths of both to enhance the performance of industrialsystems.This includes achieving wide wireless coverage,strong sensing capabilities,and fast service response,further improving the efficiency of data collection,processing,and analysis,and tap

301、ping into the potential value of industry data.Industrial intelligent production typically requires high transmission andprocessing latency,robustness,and reliability.Due to the locally deployed executioncharacteristic of industrial smart manufacturing,the expansion of base station-sidetransmission,

302、computing,algorithm resources,and capabilities is crucial under thecollaboration of 6G and GPT.It can provide lower transmission processing latencyand jitter than traditional solutions,thus ensuring higher determinism of industrial-grade signal processing.Additionally,intelligent terminals deployed

303、on industrialproduction lines may possess strong local wireless sensing,data analysis anddecision-making capabilities.Furthermore,advanced algorithms such as neuralnetworks and fuzzy control techniques can be combined and applied to product53/92formulations,service orchestration,etc.,to achieve smar

304、t manufacturing processes.This helps further improve production efficiency,reduce manual intervention,bettermeet customers large-scale personalized needs,and advance industrial productiontechnology.4.3.2“6G+GPT”Empowers Smart Healthcare6G not only better supports the massive information transmission

305、 andsynchronization related to smart healthcare but also its endogenous intelligence candirectly empower the processing and decision-making of medical information.Theemergence of GPT has overcome the limitations of traditional AI models in terms ofalgorithm maturity and sample size,reducing human in

306、tervention and monitoring andsimplifying diagnostic methods and processes.The development of medical sensors and intelligent wearable devices has driventhe reform of smart healthcare.The collaboration of 6G and GPT can be directlyapplied to medical sensors and intelligent wearable devices45,assistin

307、g in collectingpersonal physical and emotional information,providing real-time and convenienthealth monitoring,improving medical quality,and enabling users to understand theirhealth conditions.Electronic health records achieve the storage and display ofcompletepatienthistories,includingmedicalcondit

308、ions,treatmentplans,prescriptions,allergies,and other detailed information.The combination of 6G andGPT technologies enables the connection and communication of different physicaldevices and objects with the Internet,optimizing data collection methods and enablinghigh-speed transmission and synchron

309、ization of relevant information between doctorsand patients.This continuously iterates to improve the accuracy,reliability,and real-time nature of pre-diagnosis and treatment results.With the assistance of GPT,digitization and centralized analysis and management of patient information areachieved46,

310、helping establish centralized patient information repositories rapidly,enabling data-driven decision-making,and accelerating the advancement of smarthealthcare reform.4.3.3“6G+GPT”Empowers Smart TransportationSmart transportation is the result of the integration of AI and communicationtechnologies w

311、ith modern transportation systems.“6G+GPT”can bring a new levelof upgrade to urban transportation systems,promoting continuous transformation in54/92areas such as autonomous driving,unmanned aerial vehicle(UAV)delivery,unmanned taxis,and intelligent vehicle infrastructure cooperative system(IVICS).W

312、hile conducting traffic signal control coordination at the road network level,sensing of various highway networks is crucial.Currently,the sensing of highwaynetworks mainly relies on data sources such as urban checkpoints,microwave radars,and GPS positioning.The overall deployment of traffic data co

313、llection equipment isstill relatively sparse,and valuable information for constructing spatiotemporalmodels remains limited.The collaboration of 6G and GPT will fully leverageendogenoussensinganddataprocessingcapabilities,potentiallyprovidingcomprehensive multidimensional road network sensing data t

314、hrough wide coverage47.The accuracy and timeliness of traffic flow prediction are crucial for activetraffic control.Compared to traditional cloud-based AI,“6G+GPT”collaboration ismore closely related to traffic scenes,thereby providing more accurate and real-timeprediction results for estimating and

315、 predicting the state of ultra-large-scale trafficnetworks.4.3.4“6G+GPT”Empowers SmartAgricultureSmart agriculture is the application of Internet of Things technology in modernagriculture,using real-time images and videos to monitor and detect agriculturalproduction systems.Compared to traditional m

316、anual or mechanized farms,smartfarms can adopt new production operation modes based on“6G+GPT.”For instance,sensors can collect various data from farm areas,intelligently regulate crop growthenvironments to better meet crop needs,and apply various types of agricultural robotsto farming operations su

317、ch as tilling,seeding,spraying,harvesting,picking,andpackaging,further enhancing farm operation quality and efficiency while reducingmanual labor.“6G+GPT”can provide various AI service supports for smart farms,includingaccurate acquisition and transmission of sensing data based on multiple sensors,d

318、istributed AI model training based on massive data,efficient transmission andaggregation of model parameters,precise planning and flight control of UAV sprayingroutes,and autonomous driving route planning for agricultural machinery.Smartsensor devices can obtain precise data in real time,such as by

319、monitoringenvironmental data inside greenhouses.Based on this,GPT outputs solutions fortemperature control,irrigation,etc.,helping farmers achieve standardized planting55/92and truly achieve cost reduction and efficiency improvement goals.In the harvestingprocess,controlling picking robots based on“

320、6G+GPT”48enables intelligentharvesting through high-precision positioning and motion control.In addition,basedon the analysis of crop growth data,the whole life cycle of crops is controlled,planting problems are discovered and early warnings are issued in time,and losses arereduced.4.3.5“6G+GPT”Empo

321、wers Smart HomeSmart home is the result of the integration of AI technology and Internet ofThings technology in home life scenarios.Smart home systems can think,makedecisions,and schedule user habits and home environments like humans,providingconvenient,comfortable,and safe smart living.According to

322、 statistics,in the smarthome market,the overall penetration rate of AI technology was approximately 25%in2022,and it is expected to approach 50%by 2025.In categories such as intelligentvacuum cleaners,smart cameras,smart locks,and smart speakers with computervision and voice interaction functions,th

323、e penetration rate is expected to exceed 60%.“6G+GPT”can be used for home control,security monitoring,healthmonitoring,etc.Using 6G network facilities,it can perceive and analyze peoplesbehavior,gestures,and location information,and combine historical data to depict thehabits of residents.Through GP

324、T,it can understand peoples intentions and achievethe best control of all types of home devices.In terms of maintaining home security,“6G+GPT”can detect illegal intrusion,analyze and evaluate the level of danger ofintrusion actions based on household member profiles,and automatically triggeralarm ac

325、tions by the security system to prevent property loss.In terms of householdhealth monitoring,“6G+GPT”can identify and analyze sensor data in real time,enabling health monitoring and management of residents and pets.When healthindicators deviate from historical information,abnormalities can be prompt

326、ly detectedand alerted.4.3.6“6G+GPT”Empowers Digital EntertainmentThe digital entertainment industry faces the“impossible triangle”of“cost,efficiency,and quality,”which makes it difficult to simultaneously balance researchand development costs,efficiency,and product quality.The widespread applicatio

327、n ofAIGC can greatly improve productivity in planning,audio,art,programming,and56/92other aspects,shorten the overall project development cycle and personnel scale,andsignificantly reduce production costs.Virtual reality(VR),augmented reality(AR),extended reality(XR),and other technologies are trend

328、s in the new generation ofdigital entertainment.“6G+GPT”can provide fully immersive interactive scenes,support precise spatial interaction,meet human connectivity in multiple senses,andeven emotional and conscious levels.It will be a great help to realize the digitizationand intelligence of physical

329、 entities in the real world and build a new mode of digitalentertainment that integrates virtual and reality.XR requires object positioning and motion tracking,and its processing andreaction depend on AI capabilities.GPT technology can be used for future XRapplications in 6G networks,providing riche

330、r computing power and algorithmresources to ensure the execution and excellent user experience of various XRapplications49.In addition to AR and VR applications,6G networks require morepowerful network graphics and image services.In the future,this includes not onlycloud XR but also cloud gaming,sma

331、rt city,digital twin city,and digital visualization.There is a large amount of data transmission in the process of network graphics andimage processing,which requires GPT to optimize network transmission to ensuresmoothnetworkoperationandservicetimeliness.Moreover,reality-sensingtechnology requires

332、a portable terminal to sense the surrounding environment,makinginformation collection more convenient.GPT can identify,analyze,and processinformation,providing sensing and imaging capabilities beyond human capabilities interms of strong security,high precision,and low power consumption.In scenariosw

333、ith ultra-high resolution,larger bandwidth and antenna apertures are needed50.Through GPT applications that“surpass human eyes,”important information hiddenunder the skin,behind obstacles,or in the dark can be obtained.For example,in 6Gsystems,wireless radio wave sensing based on the terahertz frequency band51canachieve non-line-of-sight imaging.5.Problems Faced by the Development of“GPT+Communica