1、LARGE-SCALE AI IN TELECOMCharting the Roadmap for Innovation,Scalability,and Enhanced Digital ExperiencesWhite PaperGenAINet Emerging Technology Initiative EDITORSAli MokhAntonio De DomenicoAthanasios KarapantelakisChongwen HuangChristina ChaccourFathi AbdeldayemJuan DengKeith BallLina BariahMerouan

2、e DebbahNavid NikaeinOmar HashashQiyang ZhaoSihem CherraredCONTRIBUTORSAALTO UNIVERSITYNassim SehadBEIJING INSTITUTE OF TECHNOLOGYJie ZengYifan YangBRUNEL UNIVERSITY LONDONKehai QiuKezhi WangBUBBLERANNavid NikaeinCEA-LETIEmilio Calvanese StrinatiCENTRAL SOUTH UNIVERSITYChao ZhangCENTRALESUPLEC,UNIVE

3、RSITY OF PARIS SACLAYElissa MhannaMohamad AssaadRyad MadiSalah Eddine El AyoubiZeinab NehmeCENTRE TECNOLGIC DE TELECOMUNICACIONS DE CATALUNYARasoul Nikbakht SilabCHINA MOBILE COMMUNICATIONS CORPORATIONJiajun WuJuan DengLiexiang YueTianjiao ChenYanping LiangYingping CuiCHINA TELECOMMeiling DaiXiaoou

4、LiuXingyu Shang CHINA UNICOMJinan LiZhuang ZhouEAST CHINA NORMAL UNIVERSITYKun GuoEMIRATES INTEGRATED TELECOMMUNICATIONS COMPANY(DU)Fathi AbdeldayemMohammad Al RefaiNajla AlkaabiERICSSONAlexandros NikouAli MokhAthanasios KarapantelakisChristina ChaccourHakimeh PurmehdiJohny GemayelServeh ShalmashiTi

5、mothy MurphyEURECOMIlias ChatzistefanidisIoannis PitsiorlasRoberto MorabitoFENTECHJulien FrisonMoussab DjerrabGSMALouis PowellHUAWEIAntonio De DomenicoChenghui PengFei WangYvan PointurierZhe LiuIMEC-GHENT UNIVERSITYAdnan ShahidEli De PoorterJaron FontaineITUVishnu RamKHALIFA UNIVERSITY Lina BariahMe

6、rouane DebbahSamson LasaulceWassim HamidoucheKINGS COLLEGE LONDON Na YanNan LiSige LiuYang SuYansha Deng KOREA UNIVERSITYInkyu LeeKTH ROYAL INSTITUTE OF TECHNOLOGYAmirreza KazemiCarlo FischioneLIGHTONIacopo PoliIgor CarronNANJING UNIVERSITYBo ChengHaibo ZhouYu SunNANYANG TECHNOLOGICAL UNIVERSITYChau

7、 YuenDusit NiyatoNOKIA BELL LABSGianluca FontanesiNORTHEASTERN UNIVERSITYMaxime ElkaelMichele PoleseSalvatore DOroTommaso MelodiaNORTHWESTERN POLYTECHNICAL UNIVERSITYBo YangKATIMDheeraj SharmaDimitris Kalogiros NVIDIAKeith BallMaria Amparo Canaveras GaldonMubeen SyedSwastika DuttaORANGELecorv GwnolM

8、ehdi BoudjelliSihem CherraredQUALCOMMJinane KaramRIMEDO LABSAdrian Kliks(Poznan University of Technology)Marcin DryjanskiPawel Sroka(Poznan University of Technology)SINGAPORE UNIVERSITY OF TECHNOLOGY AND DESIGNTony Q.S.QuekZihan ChenSUN YAT-SEN UNIVERSITYXijun WangTECHNOLOGY INNOVATION INSTITUTEFaou

9、zi BaderHang ZouQiyang ZhaoULSAN NATIONAL INSTITUTE OF SCIENCE AND TECHNOLOGYHoon LeeUNIVERSITAT POMPEU FABRAGiovanni Geraci(Telefonica Research)Mohamed BenzaghtaUNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINAWeilong ChenUNIVERSITY OF GRANADAMarios KountourisUNIVERSITY OF HONG KONGHongyang

10、DuKaibin HuangUNIVERSITY OF HOUSTONZhu HanUNIVERSITY OF LEEDSMaryam HafeezMuhammad AmirSyed A.R.ZaidiZeinab NezamiUNIVERSITY OF MICHIGANZitao ShuaiUNIVERSITY OF OULUAhmed ElbakaryChaouki Ben IssaidMehdi BennisUNIVERSITY OF YORKAhmed Al-TahmeesschiHamed AhmadiSwarna Bindu ChettyVIRGINIA TECHChristo K

11、urisummoottil ThomasOmar HashashWalid SaadXIDIAN UNIVERSITYXuelin CaoYALE UNIVERSITYAli MaatoukLeandros TassiulasRex YingZHEJIANG UNIVERSITYBohao WangChongwen HuangFenghao ZhuHoward H.YangQianqian YangRongpeng LiXiaoxue YuXinquan WangYuxuan ChenZhaohui YangZhaoyang ZhangZhenyu YangZirui ChenEXECUTIV

12、E SUMMARYThe rise of generative artificial intelligence(AI)as a novel frontier that uniquely merges advanced levels of intelligence with revolutionary user experiences is redefining the AI landscape for future cellular networks.In particular,the transition towards 6G systems has introduced a myriad

13、of challenges inherent to their AI-native network design,requiring innovative solutions to enable real-time network orchestration,intelligent decision-making,and adaptive dynamic configurations.Meanwhile,the envisioned user experiences for 6G are growing increasingly complex,exceeding the capabiliti

14、es offered by vintage wireless technologies and conventional AI solutions to satisfy their advanced demands.With its disruptive impact evident across diverse fields,generative AI possesses immense potential to tackle these challenges,leveraging its exceptional capabilities to manage complex tasks,op

15、erate autonomously,and adapt seamlessly to scenarios beyond its training domain.Remarkably,generative AI provides a transformative opportunity for telecom and cellular networks to bridge this defined gap in 6G systems,thereby shifting towards a new era with cutting-edge AI innovations across the dif

16、ferent system and user levels.In essence,the introduction of generative AI into the telecom domain is primarily facilitated by a set of large-scale AI models denoted as large telecom models(LTMs).These LTMs are specifically designed to tailor the abilities of large scale AI models to effectively mee

17、t the demands of the telecom ecosystem.The goal of this white paper is to shed light on the potential of LTMs to revolutionize the telecom functions and applications from the theoretical design,implementation,and deployment perspectives,while touching on the regulatory,standardization,and industrial

18、 frameworks that govern their realization in practice.To this end,this white paper provides an explanatory overview of LTMs and their distinctive role in the radio access network(RAN)and core network,while expanding the discussion to cover several key areas that include:Fundamentals of large-scale A

19、I:Reflecting on the generative architectures and models that compose large-scale AI,along with recent trends in handling multi-modal training data,pre-training and fine-tuning techniques,alignment techniques(e.g.,reinforcement learning(RL)with human feedback),and deployment strategies on the network

20、.From large-scale AI models to LTMs:Moving beyond the state-of-the-art large scale AI models that can be vulnerable in the telecom domain,while highlighting the necessary modifications to the underlying theory of large-scale AI models to foresee the emergence of LTMs.LTMs for physical and MAC layer

21、designs:Addressing resource allocation,spectrum management,channel modeling,and mobility management,among others.LTMs for network management and optimization:Spanning adaptive monitoring and control in emerging frameworks such as Open RAN networks(i.e.,O-RAN),while highlighting the critical role of

22、leveraging LTMs with RL to enable user-centric network optimization.Datasets for LTMs:Supporting the deployment of LTMs with telecom-specific datasets and providing benchmarks with evaluation frameworks to assess the performance of LTMs.Hardware advancements and requirements for LTMs:Focusing on the

23、 role of high computing platforms to accelerate the deployment of LTMs and how the convergence of the RAN with AI plays a role in enabling LTMs over future cellular networks.New use cases and applications of LTMs:Encompassing distributed LTM frameworks over the edge,novel approaches for federated le

24、arning in LTMs,RL with LTMs interaction,intent-based management with LTMs,etc.Regulatory and ethical considerations for LTMs:Emphasizing that data governance and accountability are crucial considerations to acquire trustworthy LTM operations.Industry insights into large-scale AI models and LTMs:Incl

25、uding the current trends and ongoing projects in the industry that include large action models and on-device generative AI models,recent model breakthroughs such as TelecomGPT,and practical challenges that face LTMs such as the limited decoding rate and massive model sizes.Standardization activities

26、 and LTM roadmap:Discussing the key efforts to bring forth LTMs through focus groups within regional bodies,while setting the roadmap for LTMs by defining their roles in network infrastructure,network management,business operations with the corresponding timeline for LTMs to reach their milestones.U

27、ltimately,this white paper serves as an inaugural roadmap for LTMs in networks and provides a basis for telecom experts and industry professionals to build on the state-of-art in LTMs to push the boundaries of large-scale AI models for next-generation wireless networks.Contents1Overview of Large-Sca

28、le AI111.1Background and Definitions.111.2Introduction to Scalable,Large Telecom Models.111.3Large Telecom Models for Telecom Functions.121.4Large Telecom Models for Peripheral Functions.131.5Contribution.142Large-Scale AI in Telecom:State-of-the-art152.1Overview of Large-Scale AI in Telecom.152.2La

29、rge-Scale AI for Physical and MAC layer design.152.3Large-Scale AI for Network Management and Optimization.262.4Large-Scale AI for Un-crewed Aerial Vehicles(UAVs).272.5Large-Scale AI for Telecom Use Cases.293AI Theory of Large Telecom Models313.1From Language to Telecom Models:Challenges and Necessa

30、ry Modifications.313.2On the Interplay between Data Compression and LLMs.344Large Telecom Models Architectures&Deployment364.1Neural Network Architecture.364.2Advancements in Cross-Modality Translation:From Unimodal Processing to Multimodal Gener-ative Architectures.414.3Diffusion model.474.4Large L

31、anguage Model Training.515Datasets635.1Benchmarking and Testing Datasets.635.2Pre-Training and Instruction Tuning Datasets.675.3Advanced Dataset Design Datasets.715.4Ray Tracing and 6G-Specific Datasets.756Evaluation&Benchmarking of Large Telecom Models866.1Overview of benchmarking metrics.866.2Eval

32、uation Frameworks.896.3Capabilities and Requirements of Large Language Models applied to the Telecom domain.906.4Digital Twins for evaluation of LTMs.926.5Assess Large Language Models Telecommunications Knowledge.936.6Evaluation of Telecom Math Modeling.956.7Generating Commit Messages for Configurat

33、ion Files in 5G Network Deployment Using LLMs:Evaluation.956.8Large Model Evaluation System from Telecom Operators.987A Look into the Hardware Advancement&Requirements1037.1Background.1037.2Telecom Companies as AI Factories.1037.3The Rise of HPC-AI.1037.4Building Blocks for AI Infrastructure Deploym

34、ent.10597.5Convergence of AI and RAN.1067.6Conclusion.1078Applications and Use-Cases1088.1LTMs at the Edge.1088.2The interplay between LLM/Foundation models and federated learning.1118.3Reinforcement Learning with LTMs Interaction(9.4.1).1158.4Distributed LTMs.1198.5LTMs in Network Optimization.1238

35、.6Network automation and intent-based management with LTMs.1338.7Generating Commit Messages for Configuration Files in 5G Network Deployment Using LLMs.1358.8Use cases in optical networks.1389LTMs for Network Planning1399.1LLM as optimizer in network planning.1409.2LTMs for Immersive communication.1

36、469.3Towards Sustainable,Intelligent,and Autonomous Data Centers Enabled by Large Models.1479.4LLM-enabled Semantic Communication.1579.5LTM-enhanced Data Augmentation for Spectrum Sensing in Cognitive Radio Networks.1589.6Agents for Telecommunications(Telecom-Copilots).1639.7LTMs in Network Operatio

37、ns and Maintenance.1649.8Large telecom foundation model for the physical-layer.16510 Large Telco Models:Navigating Regulatory and Ethical Complexities16710.1 Data Governance and Accountability.16710.2 The Regulatory Landscape:EU and U.S.Differences.16710.3 Ethical and Operational Challenges in Telco

38、 AI.16810.4 Future Directions and Recommendations for Telco AI Governance.16811 Standardization Activities and Roadmap16911.1 Key Standardization Activities.16911.2 Roadmap for Large-Scale AI in Telecom.17011.3 Timeline and Milestones.17112 Industry Insights:Current Trends,Market Drivers and Barrier

39、s17212.1 The application of LLMs in Telecommunication:Current Trends.17212.2 GenAI On-Device.17912.3 Market drivers and barriers.18313 Practical challenges,Opportunities and Future Roadmap18513.1 Practical Challenges.18513.2 Innovative Opportunities.19513.3 Framework towards Next-G.19810OVERVIEW OF

40、LARGE-SCALE AI1Overview of Large-Scale AI1.1Background and DefinitionsUpcoming sixth generation of mobile networks(6G),are expected to provide more services than their predeces-sors,to an ever-growing number of users.The complexity of managing mobile networks is amplified by theirdistributed nature.

41、To address this,Artificial Intelligence(AI)algorithms,including deep learning models andsymbolic approaches must be deployed at scale across both the radio access network(RAN)and the core network.These algorithms serve as essential tools to automate network management and handle the growing complexi

42、ty ina cost-efficient way.While AI algorithms are already used in mobile networks,their functionality is typically confined to specificdomains,resulting in siloed deployments that address only narrow aspects of network performance 1.TheseAI systems struggle to generalize across diverse network envir

43、onments.More complex challenges,such as thoserequiring human-like reasoning and planning,rely on symbolic techniques,which in turn depend on human-curated knowledge bases.However,these knowledge bases tend to be brittle and difficult to scale 2.Generative AI(GenAI)algorithms have recently risen to p

44、rominence due to advancements in deep learning ar-chitectures such as transformers,which enable models to capture complex patterns and relationships within largedatasets 3.Techniques such as autoregressive modeling and diffusion processes allow these systems to generatehigh-fidelity outputs,ranging

45、from text to images,by learning the underlying data distribution.This scalability,combined with the ability to prompt or fine-tune models using minimal supervision,has significantly reduced thereliance on human-annotated data,allowing GenAI models such as Large Language Models(LLMs)to outperformsymb

46、olic models in Question Answering(QA)tasks 4,but also deep-learning based Artificial Neural Networks(ANNs)such as Recurrent Neural Networks(RNNs),in tasks such as Automated Speech Recognition(ASR),Speech Translation(ST)and Text-to-Speech(TTS)5.Additionally,research shows that LLMs,when guided bywell

47、-structured prompts that instruct them to generate intermediate reasoning steps during the output process,caneffectively solve complex arithmetic,symbolic and commonsense tasks involving advanced,System 2 reasoning6.Given this potential,it is promising to leverage GenAI in mobile networks at progres

48、sively larger scales andwith greater decision authority,ultimately aiming for fully autonomous networks.A recent survey highlighted multiple areas across all layers of mobile networks where research on the applicationof GenAI algorithms and models is actively underway 7.In this paper,we focus on Lar

49、ge Telecom Models(LTMs).LTMs are Language Models(LMs)that require large amounts of resources,for example training data,but also compute,store and bandwidth for use in mobile networks.1.2Introduction to Scalable,Large Telecom ModelsFigure 1 presents a simplified,layered architecture of a mobile netwo

50、rk,highlighting various application areasfor LTMs.The architecture includes a top-level exposure layer that enables third parties,such as enterprise cus-tomers,to interact with the mobile network.These interactions may involve specifying intents1,such as requestsfor specific quality of service(QoS)p

51、arameters(e.g.,limits on latency and packet drop rates,or guaranteedthroughput).The management layer oversees operations of the mobile network,incorporating both automatedprocesses(e.g.,leveraging AI models)and human-driven activities,such as Field Service Operations(FSO)and1In the context of autono

52、mous networks,an intent refers to a high-level,declarative goal or objective that the system aims to fulfill withoutdetailing the specific steps required.11Figure 1:Overview of applications of LTMs in a mobile network.monitoring through a Network Operations Centre(NOC).An infrastructure layer consis

53、ts of various elements ofa mobile network,such as RAN,core network,transport network between and through core and RAN,as well asthe wireless interface between RAN and User Equipment(UE)2Although LTMs are only a subset of AI models,they find broad applicability across multiple network layers.Wegenera

54、lly categorize applications into two types:those that enhance mobile network capabilities by adding newfunctionalities or improving existing ones,and those that operate peripherally to the networks core functions,interacting directly with humans.Examples of such peripheral functions include network

55、monitoring in a NOC,field maintenance operations,network design and deployment,as well as the development of network-relatedsoftware components.1.3Large Telecom Models for Telecom FunctionsFrom a top-down perspective,as illustrated in figure 1,LTMs play an important role in the exposure layer,serv-i

56、ng as a link between the customer and network domains.Specifically,LTMs can translate high-level,abstractintents expressed in the customers languagesuch as natural language in a Service-Level Agreement(SLA)doc-umentinto technical requirements that the management layer can interpret and configure in

57、the network.Theirability to understand natural language and process technical documentation makes LTMs ideal for scaling thistype of translation efficiently,thus thereby enhancing the personalization of the network experience for customers.Another facet of this personalization involves development o

58、f tailored subscription plans for customers,includingbilling structures and feature offerings that are specifically adapted to customer needs,rather than being gener-ically designed by the mobile network operator.In another example of personalization,fa LTM agents can beused to abstract complexity o

59、f telecom-specific network exposure Application Program Interfaces(APIs)from thecustomer,such as those specified for Network Exposure Function(NEF)and Service Capability Exposure Func-tion(SCEF)by 3GPP.In this case,a customer may use an abstract prompt,such as a natural language interfaceto request

60、services from the network,such as monitoring of its UE,and an LTM can translate this request to asequence of API calls.In the management layer,LTMs can facilitate the automation of network operations management.This encom-passes two key aspects:first,the support LTMs can provide for field services,s

61、uch as the installation,commission-2In Third Generation Partnership Project(3GPP)nomenclature,UE are the user terminals accessing the network(e.g.,mobile phones).12ing,and maintenance of Radio Base Stations(RBSs).In this context,LTMs can serve as digital assistants,aidingfield service personnel duri

62、ng on-site tasks.Often,RBSs are located in remote areas,making it costly to sendpersonnel for fault repairs or scheduled maintenance.Additionally,staff must be proficient not only in the specificproducts on-site to diagnose issues and identify root causes but also must undergo safety training,especi

63、ally sincefaults may occur on radio tower tops.LTM assistants can help mitigate some of these expenses by offering real-time assistance based on situational assessments to field service engineers.The second aspect involves automatingtrouble reporting in the NOC.Typically,identifying issues requires

64、the aggregation and analysis of diverse datasets,including logs from various systems and visual data.LTMs,with their capacity to be effectively promptedfor this information through approaches like Retrieval-Augmented Generation(RAG)8,can process the data togenerate a trouble report in natural langua

65、ge,as well as provide recommendations for fault resolution.LTMs canalso be used for policy-based management,as they can dynamically interpret and implement complex networkpolicies in real time.In the infrastructure layer,we identify two separate uses for LTMs.The first is content generation,whereinL

66、TMs,such as Generative Adversarial Networks(GANs),can be utilized to create synthetic data to augment realnetwork observations.This is particularly useful in cases where real data is scarce or the network is overloadedwith communication tasks,preventing it from observing and/or transporting actual d

67、ata to where it is needed.Forexample,if data originates from UE,such as Channel State Information(CSI)reports,then the availability of thisdata depends on the presence of UE.However,there may be instances where UE are unavailable,such as whenthe network aims to generate a model of the wireless chann

68、el in a specific area or to create a coverage map.Agenerative model can be used in such cases to augment real-life network observations.The other aspect involvescontent delivery over the air interface,particularly for media-rich,high-resolution content like high-definitionvideo.In 6G,adoption of Ext

69、ended Reality(XR)applications is anticipated to rise,alongside the introduction ofnew devices such as virtual reality and augmented reality headsets.Technologies like semantic communication,which leverage encoder-decoder generative models such as Variational Autoencoders(VAEs)and transformersinstead

70、 of transmitting raw data,can significantly reduce bandwidth requirements per XR session.This reductionwould enable the network to accommodate a greater number of concurrent sessions.1.4Large Telecom Models for Peripheral FunctionsIn addition to being embedded in the mobile network itself,LTMs can a

71、lso be used for functions that supportthe mobile network.One key area where LTMs can help is in network deployment and planning.They cananalyze historical and real-time data to determine the best locations for new base stations.This analysis includeslooking at factors like population density,user be

72、havior,and existing network coverage.By understanding whereusers are and how they use the network,LTMs can suggest locations that will maximize coverage and minimizeinterference.Additionally,LTMs can assist in designing network architectures by simulating different scenarios.For instance,they can mo

73、del how changes in user demand or network traffic might affect performance.This helps engineersidentify potential problems and make adjustments before actual deployment,saving time and resources.In software development,LTMs can automate routine tasks such as coding,testing,and documentation.Forexamp

74、le,they can take user requirements written in plain language and generate code snippets that fulfill thoserequirements.This can speed up the development process and reduce the chance of errors.Moreover,LTMs can help create synthetic data for testing.When real data is limited or unavailable,this synt

75、heticdata can simulate real-world conditions,allowing developers to test their software thoroughly.They can alsogenerate various test scenarios to ensure that network applications perform well under different conditions.13Another application of LTMs is as digital assistants,i.e.chatbots that assist

76、users such as customers and mobilenetwork operator personnel in navigating complex mobile standards,as well as product and network documenta-tion.These chatbotscanprovide instantanswers toqueriesabout technicalspecifications,regulatoryrequirements,or best practices in mobile network operations.By of

77、fering easy access to this information,LTMs can help reducethe time engineers spend searching for documents or standards,allowing them to focus more on critical tasks.Overall,using LTMs for network deployment,planning,and software development can lead to more efficientoperations and better service f

78、or users.By analyzing data,simulating scenarios,automating tasks,and providingquick access to information,LTMs can support network engineers and developers in building and maintainingrobust mobile networks.1.5ContributionMobile networks consist of a large number of interconnected nodes,which generat

79、e and transmit large amountsof data.This distributed architecture is challenging in context of large-scale LTM deployment.On a high levelthis paper aims to provide the reader with an understanding on the following challenges related to deployment ofLTMs.Understanding the background context,including

80、 a review of state of the art(SoA)of LTMs algorithms anddeployment architectures.Understanding the use-cases and mobile network infrastructure requirements for deployment of LTMs inmobile networks,both for training but also for inference.Understanding the current capabilities of network infrastructu

81、re and UE.to host and train LTMs.Describing metrics and datasets to train LTMs and evaluate the accuracy and credibility of their responsesduring inference-time.Understanding the regulatory framework,standardization activities and market trends for large-scale adop-tion of LTMs.This paper functions

82、as a multi-disciplinary guide for large-scale deployment aspects of LTMs.This is donein form of a deep-dive into SoA tools and methods for each of the scalability aspects highlighted above.Theremainder of this section provides more information on each of these aspects and includes references to othe

83、rsections of the paper,allowing readers to directly access the topics they are interested in.14LARGE-SCALE AI IN TELECOM:State-of-the-art2Large-Scale AI in Telecom:State-of-the-art2.1Overview of Large-Scale AI in Telecom6G network will be intelligent networks capable of responding in real-time to em

84、erging demands and changingenvironments.It will support a wide range of applications and scenarios,such as the Internet of Things,smartgrid,autonomous vehicles,intelligent agriculture,and so on.The design of Telecom network is transformingfrom pure communication oriented towards intent and goal orie

85、nted.It will not only deliver information to meetcertain QoS requirements(throughput,latency,reliability),but also plan,configure,and optimize functionalitiesand protocols to environment conditions and user demands.2.2Large-Scale AI for Physical and MAC layer designIn this section,we discuss some fu

86、ndamentals of the physical layer which would be enhanced by generative largemodels.2.2.1AI-Based MIMO DetectionMIMO detection refers to the process of recovering transmitted signals at the receiver end in a MIMO commu-nication system,which is critical because,while MIMO allows for the simultaneous t

87、ransmission of multipledata streams over the same frequency band,the signals from different antennas can interfere with each otherdue to multipath propagation and channel fading.The primary goal of MIMO detection is to separate thesetransmitted signals and recover the original data accurately.The ma

88、in challenges in MIMO detection arise frominterference between signals transmitted from different antennas,noise and other channel impairments,and thehigh-dimensional detection in systems with many antennas like massive MIMO.Depending on the number ofantennas and the channel conditions,various detec

89、tion algorithms are employed,each with trade-offs in terms ofcomplexity and performance.The current methods for MIMO detection range from traditional linear approaches,like Zero Forcing and MMSE,to advanced non-linear(like Maximum Likelihood and Sphere Decoding),iterative,and machine learning-based

90、techniques.Table 2 lists these methods.Generative AI and large models(like deep learning)can significantly improve MIMO detection by offering ad-vanced capabilities for handling complex and dynamic communication environments.These AI-driven methodsare particularly useful in dealing with the high dim

91、ensionality,non-linearity,and real-time constraints typical ofMIMO systems.Below are ways in which generative AI and large models can help improve MIMO detection:Learning-Based Detection:Deep learning models,particularly large models like DNNs,can be trained to mapreceived signals directly to transm

92、itted symbols,bypassing traditional detection techniques.This is especiallyvaluable in highly complex and dynamic MIMO systems such as massive MIMO,where traditional algorithmsstruggle with non-linearity and high interference levels.Generative models can learn complex non-linear rela-tionships betwe

93、en transmitted and received signals,making them highly suitable for MIMO systems operatingin complex environments,such as those with fading,interference,and mobility.Unlike traditional methods thatrequire specific model assumptions,they also have easy real-time adaptation and deep learning models ca

94、n gener-alize well to various channel conditions after training.As we observed,in large-scale MIMO systems,traditionalmethods can become computationally expensive.AI-based models can efficiently scale and handle these largesystems,reducing computational load and improving detection speed.Generative

95、Models for Channel Estimation:Generative models such as GANs and VAEs can help enhance chan-nel estimation and modeling in MIMO systems,which is crucial for accurate detection.These models can simulaterealistic wireless channel environments and assist in generating more accurate channel matrices,imp

96、roving de-15tection performance.GANs can be used to generate synthetic,yet realistic,channel conditions based on a smallamount of real-world data,allowing the MIMO detector to adapt to various channel conditions dynamically.Table 2:Summary of the current ML detection methodsCategoryMethodApproachAdv

97、antageDisadvantageLinearDetec-tionZeroForcing(ZF)Eliminates multi-antenna in-terference by inverting thechannel matrix.Simple and easy to imple-ment.Good for high SNRenvironments.Amplifies noise,poor per-formance in noisy or ill-conditioned channels.MinimumMeanSquareError(MMSE)Balances between inter

98、fer-ence suppression and noiseamplification.Better than ZF in noisychannels.Slightly more computation-ally complex than ZF.MatchedFilter(MF)MaximizesthereceivedSNR for each transmittedstream.Very simple and computa-tionally efficient.Suffersfrominterferencebetween data streams.Non-Linear De-tectionS

99、uccessive Inter-ference Cancella-tion(SIC)Detects the strongest signalfirst,subtracts its contribu-tion from the received sig-nal,and then detects the re-maining signals iteratively.Interference reduction be-tween streams.Error propagation can occur;the order in which the sig-nals are detected affec

100、ts per-formance.Maximum Likeli-hood(ML)De-tectionMinimizestheEuclideandistancebetweenthereceivedsignalanditsestimated version based onthe channel matrix.Minimizing BER.Computationalcomplexityincreasesexponentiallywith the number of transmitantennasandmodulationorder.Impractical for largesystems.Sphe

101、re Decoding(SD)Reduces the complexity ofML detection by search-ing within a certain spherearound the received signal.Near-ML performance withlower complexity.Still computationally expen-sive for large MIMO sys-tems,though much less sothan ML.Iterative Detec-tionTurbo DetectionInvolves iterative feed

102、backbetween the equalizer(ordetector)and the decoder.Closetooptimalperfor-mance.Requires multiple iterations,increasing detection delayand computational load.BeliefPropaga-tion(BP)Uses a factor graph andperforms probabilistic infer-ence,passing”messages”betweennodestorefineprobability estimates.Effi

103、cientinstructuredMIMO systems,such asLDPC-based systems.Complexity increases withthenumberofantennas;convergenceissuescanarise.CompressedSensing-BasedDetectionExploits the sparsity of thesignal in systems where thenumber of transmit antennasexceeds the number of re-ceive antennas.Suitableforunderdet

104、er-mined MIMO systems.Works best when the trans-mittedsignalhassparsecharacteristics.HybridDetec-tion TechniquesCombines multiple detec-tion methods to balance be-tween complexity and per-formance.Flexibilitytodifferentchannel conditions.A goodtrade-offbetweenperfor-mance and complexity.Increased co

105、mplexity.Moreover,this generated data could be used for supervised learning-based MIMO detectors,enhancing theirrobustness to different signal and channel scenarios,even when limited real-world data is available.By predictingchannel states in real-time,they reduce the complexity of explicit channel

106、estimation steps required for MIMOdetection.AI-Enhanced Joint Detection and Decoding:Deep learning-based architectures like DetNet(Detection Network)and RNNs can integrate MIMO detection and decoding processes.AI models can jointly perform both MIMOdetection and channel decoding tasks,leveraging the

107、 correlation between these steps to enhance overall systemperformance.By leveraging the joint detection-decoding ability,these models reduce the overall complexity ofprocessing while improving the detection performance.AI-driven architectures that mimic turbo decoding allowfor efficient error correc

108、tion and more accurate signal recovery in the presence of noise and interference.Adaptive Detection Using Reinforcement Learning(RL):this category of learning models can be employed todynamically adapt the detection strategy based on real-time feedback from the environment.RL agents can learnoptimal

109、 detection strategies over time,adjusting to changing channel conditions or system parameters to maintain16performance.This is especially useful in fast-changing channel conditions,where static detection strategies maynot perform well.RL-based models can optimize MIMO detection without requiring a c

110、omplete model of thewireless environment,learning the best actions to minimize BER or maximize throughput.They approaches cangradually improve performance without requiring vast amounts of training data upfront,unlike supervised deeplearning models.2.2.2Channel coding/decodingChannel codecs(channel

111、coder and decoder)are essential techniques in digital communications used to detect andcorrect errors that may occur during data transmission over noise,interference,and other channel impairments.The goal of channel coding is to enhance the reliability of data transmission by introducing redundancy

112、into thetransmitted information,allowing the receiver to detect and correct errors without the need for retransmission.Itincludes three parts:the channel encoding adds redundancy to data to enable error detection and correction.Thechannel decoder uses redundancy to detect and correct errors in the r

113、eceived data.Finally,error detection andcorrection which improves reliability by detecting and correcting errors in transmitted data.Current channel codec methods are advanced techniques and they have evolved significantly to meet the demandsof modern communication systems like 5G,satellite communic

114、ations,and digital storage.The main methodsinclude block codes,convolutional codes,turbo codes,and low-density parity-check(LDPC)codes.Each methodhas its own coding/decoding strategy based on the type of errors it aims to detect or correct.Below is a shortexplanation of the most common methods:Block

115、 Codes:Hamming,Reed-Solomon codes(used for bursting error correction,storage systems).Convolutional Codes:Used in real-time communication,Viterbi decoding is widely applied in satellite andmobile systems.Turbo Codes:Highly efficient for wireless systems,iterative decoding(4G,5G).LDPC Codes:Widely us

116、ed in modern wireless systems(Wi-Fi,5G)and storage systems due to their near-capacity performance and efficient decoding.Polar Codes:The latest in channel coding,selected for 5G control channels,offering efficient decodingthrough successive cancellation.HARQ:Combines ARQ and FEC,retransmitting corru

117、pted packets with additional redundancy for im-proved error correction.Large and generative models can significantly enhance the efficiency and performance of channel coding/decodingby introducing data-driven techniques to improve error correction,decoding algorithms,and coding optimization.These im

118、provements have been explored in recent research,with AI models showing promise in overcoming someof the traditional limitations of classical coding techniques.Heres how generative AI and large models can help:Improving Decoding Algorithms:Generative AI and large deep learning models can be used to

119、optimize andimprove decoding algorithms,particularly in soft-decision decoding and iterative decoding(e.g.,for Turbo codes,LDPC codes).Neural networks can be trained to learn the decoding process from received noisy signals andautomatically perform error correction.Deep learning decoders can replace

120、 conventional methods like the Viterbior belief propagation algorithms,resulting in more robust and faster decoding.RNNs,or even more advancedmodels like LSTMs,can learn sequential data and are particularly well-suited for decoding convolutional codes.These AI-based models can outperform traditional

121、 methods when trained on large datasets with varying noise17levels.Finally,generative models can predict the likelihood of each received bit and perform soft decoding,whichis particularly useful for LDPC and turbo codes.By leveraging AI,decoders can more efficiently handle noisyand corrupted signals

122、 and find the most likely transmitted sequence with higher accuracy.Designing New Codes:Generative AI can be employed to design new coding schemes that outperform traditionalcodes like LDPC,Turbo,or Polar codes.By treating coding as a generative process,AI models can learn to createhighly efficient

123、codes optimized for specific communication environments.For instance,instead of relying onpredefined codes like Reed-Solomon or Hamming,neural networks can generate codes tailored to specific channelcharacteristics or noise levels,offering better performance in specific applications.AI models like a

124、utoencoderscan be trained to simultaneously learn encoding and decoding procedures,allowing the discovery of new codesthat can handle errors more efficiently.Autoencoders can capture complex channel noise characteristics and adaptthe code to the channel.AI models can also create adaptive error-corre

125、ction codes that evolve based on thecurrent communication environment.This adaptability is useful in scenarios where channel conditions changedynamically(e.g.,5G networks).Optimizing Hybrid Automatic Repeat Request(HARQ):Generative AI models can optimize HARQ systems bypredicting when retransmission

126、s are necessary and adjusting the coding redundancy dynamically to reduce theneed for retransmissions,saving bandwidth and improving throughput.RL can optimize retransmission decisionsin HARQ by learning from the networks feedback.AI models can predict the optimal amount of redundancyneeded for each

127、 transmission,improving efficiency.Generative models can adaptively generate parity bits basedon the channel conditions,instead of relying on fixed redundancy patterns.This adaptive approach allows HARQsystems to be more efficient,reducing retransmissions in challenging conditions.Moreover,GANs can

128、create syn-thetic data that represent possible transmission errors,allowing the system to simulate different channel conditionsand learn optimal HARQ strategies under various error rates.Enhancing Polar Codes for 5G:Generative AI can enhance Polar Codes,which are used in 5G for control chan-nels,by

129、improving the successive cancellation decoding(SCD)process or designing more efficient decodingarchitectures.AI models can be applied to successive cancellation list decoding(SCLD),improving the decision-making process during decoding by better estimating the probability of each bit.This leads to be

130、tter error cor-rection performance,especially in short-block-length polar codes.In addition,polar codes rely on frozen bitsto reduce complexity.Generative AI can dynamically optimize the selection of frozen bits based on the channelconditions,improving the overall code efficiency and error correctio

131、n capability.AI-driven polar codes can beoptimized for specific use cases and communication environments.Optimizing LDPC Codes:LDPC(Low-Density Parity-Check)codes are widely used in wireless and storage sys-tems.Generative AI can optimize LDPC decoders by improving belief propagation(BP)algorithms,o

132、r evendeveloping entirely new graph-based decoding architectures based on learned data.Enhancing the belief prop-agation algorithm is possible by learning better message-passing strategies between variable and check nodes,improving convergence speed and decoding accuracy.Moreover,DNNs can be trained

133、 as decoders that simulatethe message-passing process of LDPC decoding but with higher resilience to noise and interference.2.2.3Resource allocationResource allocation for the physical layer in wireless communication systems involves distributing and managingresources like power,time,frequency,and s

134、patial domains to maximize data throughput,minimize interference,and improve the overall network quality of service(QoS).Efficient resource allocation is critical for ensuringthat the physical layer can support higher data rates,better reliability,and lower latency,especially in complexnetworks like

135、 5G and beyond.The main components of resource allocation at the physical layer are power alloca-tion,subcarrier allocation,time and spatial resource allocations,adaptive modulation and coding,and interference18management and coordination.The current methods focus on optimizing power,frequency,time,

136、and spatial re-sources to enhance performance in terms of throughput,latency,and energy efficiency.The resource allocationmethods at the physical layer are especially vital in complex systems like 5G,6G,and massive IoT,where resourceallocation must be adaptive and scalable.However,these methods cont

137、inue to evolve with advances in AI andML.Generative AI and large models can enhance resource allocation in the physical layer by improving real-timedecision-making,predictive analytics,and adaptive optimization.These technologies provide a data-driven,adap-tive,and proactive approach to managing res

138、ources,allowing for enhanced performance,efficiency,and resiliencein complex and high-density networks such as 5G and future 6G systems.Dynamic Power Allocation:Generative AI can learn and predict optimal power allocation strategies based onreal-time and historical network conditions,resulting in mo

139、re efficient use of energy and better interference man-agement.RL can continuously optimize power allocation by learning from network feedback and prioritizingpower efficiency.Frequency and Subcarrier Allocation:AI models can learn and allocate frequency resources more effectivelyin systems like OFD

140、MA by predicting user demand,channel quality,and minimizing interference among users.By leveraging historical channel data,generative models can predict channel quality for users,improving theefficiency of frequency allocation.AI models can also treat resource allocation as a multi-agent game,optimi

141、zingallocation strategies among users competing for spectrum.Time Slot Allocation and Scheduling:Large AI models can improve scheduling strategies in time domain resourceallocation,handling real-time variations in user demand and network conditions more adaptively.RL modelscan dynamically allocate t

142、ime slots based on network feedback,optimizing for throughput,and reducing latency.Moreover,AI models can balance user demand and fairness,providing tailored time slot allocations based onpriority and resource availability.Spatial Resource Allocation with Beamforming and MIMO:Generative models and r

143、einforcement learning canoptimize beamforming and MIMO spatial resources,making it easier to manage multi-user interference and boostspectral efficiency.Generative models can predict optimal beam directions to maximize signal quality and reduceinterference for each user.AI models group users with si

144、milar spatial characteristics,optimizing spatial resourceallocation and minimizing interference.Adaptive Modulation and Coding(AMC):AI-based models can predict channel conditions and adapt modulationand coding schemes dynamically,optimizing throughput and error rate based on real-time conditions.For

145、 ex-ample,NNs predict channel states and automatically select the most appropriate modulation and coding scheme,maximizing data rates;or,RL models dynamically adapt AMC schemes based on channel feedback,ideal forrapidly changing wireless environments.Interference Management and Coordination:Generati

146、ve AI models can be used to simulate and predict inter-ference patterns interference under various configurations,allowing for preemptive adjustments to resources andenhancing network performance under high user density.Also,ML can improve CoMP by predicting inter-cellinterference,enabling collabora

147、tive resource allocation among base stations.2.2.4Reconfigurable intelligent surfaces(RIS)Using higher frequency bands poses challenges with electromagnetic wave obstructions.RIS offer a solution byactively shaping and controlling electromagnetic waves to improve wireless network performance.Compris

148、ingpassive elements or small antennas,RIS can adjust the phase,amplitude,and sometimes polarization of incomingsignals,allowing them to steer,reflect,or scatter radio waves without needing active transmission power.RIS19acts like a smart mirror,reflecting signals and steering them by adjusting the p

149、hase.It enhances signal strengthat specific locations,focuses signals into narrow beams for minimal energy loss,and optimizes multiple signalpaths in wireless communication,reinforcing desired paths and reducing interference.RIS can provide improvedcoverage and capacity,enhances the energy efficienc

150、y with relatively simple and inexpensive HW compared to tra-ditional infrastructure.Nevertheless,controlling thousands of elements on RIS requires sophisticated optimizationalgorithms that can adapt in real-time to changes in the environment.Current methods for RIS algorithms focus on how to control

151、 and manipulate the individual elements of the surfaceto achieve desired signal processing outcomes.These methods generally fall into categories based on how theelements are designed and how they are controlled.Here are the main methods and approaches currently used inRIS:Passive vs.Active RIS(energ

152、y consumption vs.signal amplification)Phase Shifting(discrete vs.continuous)Programmable Meta-materials(electrical control being the most common)Reflective vs.Transmissive RIS(based on signal interaction)Hybrid RIS(combining passive and active elements)Digital vs.Analog Control(accuracy vs.simplicit

153、y)AI-Driven Optimization(for dynamic adaptation)Wireless Power Transfer(for self-powered systems)High-Frequency RIS(mmWave and THz bands for next-gen communication)Generative AI and large models like deep learning and reinforcement learning can significantly enhance the per-formance,optimization,and

154、 deployment of RIS in wireless communication.the complexity of optimizing RIS-assisted systems requires advanced solutions,and generative AI and large models can provide innovative ways toaddress these challenges.They improve real-time adaptive control through RL and deep generative models enablesdy

155、namic and efficient beamforming.Provide high quality channel estimation and prediction using GANs and largemodels allow RIS to anticipate environmental changes and optimize signal paths.Moreover,RIS deployment andenergy efficiency optimization by leveraging AI-driven simulations and forecasting meth

156、ods reduces operationalcosts and improves network performance.These models also can contribute to the following areas which eitherdirectly or indirectly can help RIS:Channel Estimation and Reflection Coefficient Optimization:One of the key tasks in RIS-assisted systems isoptimizing the reflection co

157、efficients(phase shifts)of the RIS elements to maximize SNR at the receiver.Thisrequires accurate CSI between the base station,RIS,and users,which can be challenging to obtain in practicalscenarios,especially for large surfaces with many elements.Generative models can generate realistic CSI data,hel

158、ping improve channel estimation accuracy in RIS systems.Furthermore,deep learning models can directlylearn the mapping between the channel conditions and optimal reflection coefficients,leading to better and fasteroptimization compared to traditional iterative algorithms.End-to-End Learning for RIS-

159、Assisted Communication:Optimizing the RIS requires coordination between thetransmitter,the RIS,and the receiver.Traditional methods rely on separate optimization stages,which may notbe globally optimal.Moreover,RIS elements are passive and cannot actively adjust their behavior based on20real-time fe

160、edback,making real-time adaptation challenging.End-to-end deep learning models can be used tojointly optimize the entire system,including the transmit beamforming,RIS phase shifts,and receiver processing.RL or Deep RL can further enable RIS to dynamically adjust its configuration based on real-time

161、environmentalfeedback.Generative AI for Environment and Channel Modeling:RIS systems heavily depend on the surrounding environ-ment,including obstacles,reflectors,and scattering objects.Accurate environment modeling is essential to predicthow RIS should be configured to enhance the wireless signal.H

162、owever,real-world environments are complex anddynamic,making it difficult to model them accurately in real-time.Generative AI models,such as GANs andVAEs,can be used to create realistic 3D models of the wireless environment as well as the channel conditions,allowing RIS systems to be trained and tes

163、ted in a wide variety of scenarios without requiring costly real-worldmeasurements.Data-Driven Optimization of RIS Hardware Parameters:RIS devices are typically designed with fixed hardwarecharacteristics,such as the number of reflecting elements and the phase-shifting capabilities.However,the hard-

164、ware design may not always be optimal for every deployment scenario,especially in dynamic environments withvarying interference,mobility,or user density.Data-driven optimization of hardware parameters,based on gen-erative large models,allows RIS devices to be more adaptable and efficient,ensuring th

165、at they are designed formaximum flexibility and performance in real-world scenarios.By training AI models on large datasets that reflectdifferent deployment scenarios,the hardware design can be fine-tuned to maximize the performance across a widerange of environments.AI for Real-Time Control of RIS

166、in Mobile Environments:In mobile environments,where users and objects areconstantly moving,the wireless channel can change rapidly,and the optimal RIS configuration may need to beupdated in real-time.Traditional optimization algorithms are often too slow to keep up with these rapid changes.RL-based

167、approaches enable RIS to adapt quickly and efficiently to real-time changes in the environment,suchas user mobility,changes in interference,or obstacles.This improves the robustness and performance of RIS indynamic scenarios.Joint RIS and Base Station Beamforming Using AI:The coordination between th

168、e RIS and the base station iscritical to optimizing the overall system performance.Beamforming at the base station and phase shifting at theRIS need to be jointly optimized,which is computationally intensive and challenging in practice.Deep learningmodels can be used to jointly optimize both the bea

169、mforming at the base station and the phase shifts at the RIS,considering the wireless channel characteristics and user positions.This approach allows the system to maximizethe received signal power at the user while minimizing interference to other users,reducing the BER,improvingthroughput,and enha

170、ncing coverage in challenging environments.Generative Models for RIS-Assisted Beam Prediction:In systems like mmWave and massive MIMO,beam align-ment between the transmitter and receiver is crucial,and RIS can assist by reflecting signals in optimal directions.Generative models,such as GANs,can be u

171、sed to predict the optimal beams and RIS configurations based on par-tial channel information or user mobility patterns.These models can generate likely channel realizations,allowingthe system to make more accurate beam predictions,improve the accuracy of beam alignment in RIS-assisted sys-tems,redu

172、cing the time needed for beam training and improving throughput in dynamic environments.Federated Learning for Distributed RIS Control:In large-scale deployments with multiple RISs,centralized con-trol may become infeasible due to communication overhead,privacy concerns,and scalability issues.Distri

173、butedlearning mechanisms such as federated learning can be applied to enable distributed RIS control.Each RIS canindependently learn from local data,while periodically sharing model updates with a central server.This allowsthe global model to improve without requiring raw data exchange.This will imp

174、rove scalability and privacy.It21also reduces the need for constant communication between the RISs and the central controller.2.2.5MIMO-IM(Index modulation)MIMO-IM is an advanced communication technique that combines MIMO technology with Index Modulation(IM),with the goal of improving spectral and e

175、nergy efficiency by modulating both data symbols and antenna in-dices for information transmission.Traditional MIMO system employs all the antennas while MIMO-IM activatesonly a subset of antennas the indices of the active antennas carry additional information.Although MIMO-IMincreases the spectral

176、efficiency without requiring additional power or bandwidth,there are major performancechallenges.Jointly detection of both the data symbols and the active antenna indices requires more sophisticateddetection algorithms which can increase computational complexity of the detection,particularly for lar

177、ge-scaleMIMO systems.MIMO-IM highly depends on accurate CSI estimation for the correct detection,which becomeseven more challenging due to the dynamically changing of the active antennas.Finally,MIMO-IM is sensitive tointerference,especially when deployed in dense networks.Managing interference whil

178、e detecting both antennaindices and symbols requires advanced signal processing techniques.CurrentmethodsforMIMO-IMsystemsaimtoimprovespectralefficiency,energyefficiency,anddetectionperfor-mance by leveraging the unique features of IM while addressing the inherent challenges of detection complexity,

179、interference,and channel estimation.Table 3 summarizes these methods,their advantages,and limitations.Theinformation in this table indicates although deterministic AI is helpful,their training and accessing to the rightvolume of the data are main problems.In contrast,Generative AI and large models c

180、an significantly enhanceMIMO-IM systems by optimizing various aspects such as channel estimation,signal detection,resource alloca-tion,and performance under complex scenarios.The optimization of MIMO-IM systems is highly complex,andgenerative AI can bring transformative benefits by addressing these

181、challenges.Gen AI and the large models can help MIMO-IM in the following areas:Improving Channel Estimation in MIMO-IM Systems:MIMO-IM systems rely on CSI for efficient detection and performance.Traditional channel estima-tion methods,especially in high-mobility or fading environments,may not provid

182、e the necessary accuracy,leadingto sub-optimal performance.Generative models,such as VAEs or GANs,can be used to generate synthetic CSIdata to enhance the training and accuracy of deep learning models for channel estimation.Furthermore,deeplearning models can be trained to estimate the channel more

183、efficiently in a data-driven manner,bypassing theneed for traditional,complex channel estimation algorithms.By leveraging Gen AI to model the channel,MIMO-IM systems can benefit from more accurate and robust channel estimation,leading to improved signal detectionand overall system performance.Enhanc

184、ing Signal Detection with Deep Learning:MIMO-IM systems introduce an additional layer of complexityin the signal detection process because data is transmitted not only through signal modulation but also throughthe indices of the activated antennas.Traditional detection techniques(e.g.,Maximum Likeli

185、hood detection)maysuffer from high computational complexity,especially for large MIMO-IM configurations.Deep learning models,such as CNNs or RNNs,can be trained to perform signal detection in MIMO-IM systems.These models can learnthe complex relationships between the transmitted signals,the antenna

186、indices,and the received signal,resultingin efficient and accurate detection.AI-based detection methods can significantly reduce the computational com-plexity while improving detection accuracy,particularly in scenarios with high interference or complex channelconditions.Joint Optimization of Antenn

187、a Selection and Modulation:In MIMO-IM,selecting the active antennas and mod-ulating the signal simultaneously can be a complex task,as it involves a large combinatorial search space.Tra-ditional algorithms may not efficiently find the optimal antenna selection and modulation strategy,especially inla

188、rge-scale MIMO systems.RL or Deep RL can be applied to jointly optimize antenna selection and modulation22in MIMO-IM systems.By learning the optimal policy through interaction with the environment,RL agents canadapt to changing channel conditions and interference patterns,ensuring optimal resource u

189、sage and performance.RL-based optimization methods allow MIMO-IM systems to dynamically adapt to varying conditions,leading toimproved spectral efficiency,energy efficiency,and overall performance.End-to-End Learning of MIMO-IM System Components:MIMO-IM systems consist of multiple components,suchas

190、transmitter design,channel estimation,signal detection,and decoding.Optimizing each component separatelymaynotleadtogloballyoptimalperformance,astheinteractionsbetweencomponentsarecomplexandnon-linear.End-to-endlearningcanbeappliedtojointlyoptimizethe entireMIMO-IMsystem,fromtransmissiontodetectiona

191、nd decoding.By training a neural network on the full system,the model can learn the optimal configurations foreach component,considering their interactions.This learning provides a unified approach that optimizes the entireMIMO-IM system,leading to better performance compared to isolated optimizatio

192、n of individual components.Table 3:Summary of current MIMO-IM methodsMethodDescriptionAdvantagesLimitationsSpatial Modulation Only one antenna is activated at a time totransmit the data symbol The index of the active antenna carriesadditional information Low complexity Reduced interference Reduced p

193、ower consumption Suffers from limited spectral efficiencyGeneralizedSpatialModulation A subset of antennas is selected and canbe activated simultaneously Improved spectral efficiency Increased detection complexityQuadrature Spatial Mod-ulation Information is encoded in both spatial(antenna indices)a

194、nd the signal phase Better spectral efficiency without increas-ing complexity Requires precise phase synchronizationbetween transmitter and receiver(im-practical)Enhanced Spatial Modu-lation Combines Spatial Modulation with tradi-tional modulation techniques Both antenna indices and transmittedsymbo

195、ls are modulated using higher-order modulation schemes Increased data rate Higher power requirements MorecomplexdetectionalgorithmsatthereceiverDual-Mode Index Mod-ulation In addition to antenna indices,antennamodes are also selected Antenna modes are ON,OFF,or trans-mitting with different power lev

196、els More transmitted bits by utilizing differ-ent transmission modes Increased complexity of transmitter andreceiver designCompressedSensing-BasedMIMO-IMDetection Compressed sensing techniques exploitthe sparse nature of the transmitted sig-nal Computationally efficient,particularlyfor large-scale s

197、ystems High sensitivity to sparsity of signal andquality of channel estimationHybrid MIMO-IM Tech-niques Combination of advancedtechniquessuch as OFDM,NOMA,or MassiveMIMO Enables MIMO-IM to be applied inbroader scenarios,such as in 5G net-works Increased system complexity Requires more advanced hard

198、ware or sig-nal processing capabilitiesIterative Detection andDecoding Receiver iteratively refines its estimatesof transmitted symbols and active an-tenna indices Useful in low SNR or high interferencescenarios Lower bit error rates Increased processing time Not suitable for low-latency application

199、sErrorControlCodingwith MIMO-IM Error control coding,such as LDPC orTurbo Codes,integrated with MIMO-IM Enhanced robustness against errors Added complexity of encoding and de-coding Increased computational loadDeepLearning-BasedMIMO-IM Detection CNN and RNN models learn relation-ships between receiv

200、ed signals,active an-tenna indices,and transmitted symbolsfor efficient detection in challenging en-vironments Highly adaptable to different channelconditions,interferencepatterns,andsys-tem configurations Requires large amounts of data for train-ing High computational complexity,espe-cially for lar

201、ge-scale systemsGenerative AI for Channel and Interference Simulation:Designing and testing MIMO-IM systems in realisticenvironments can be challenging due to the wide variety of channel conditions,interference patterns,and mo-bility scenarios.Acquiring large amounts of training data for AI models i

202、n such environments is expensive and23time-consuming.Generative AI models,such as GANs,can be used to simulate realistic channel conditions andinterference patterns,allowing MIMO-IM systems to be trained and tested in a wide range of scenarios withoutneeding extensive real-world measurements.These s

203、ynthetic datasets can help train AI models that perform chan-nel estimation,signal detection,and interference management.Using generative AI for data augmentation allowsMIMO-IM systems to be more robust to real-world conditions,leading to better performance when deployed indiverse environments.AI fo

204、r Low-Latency Detection and Resource Allocation:MIMO-IM systems,particularly in real-time applicationssuch as 5G and beyond,require low-latency detection and resource allocation to meet the stringent performancerequirements.Traditional algorithms may introduce significant delays,especially as the si

205、ze of the system grows.Lightweight AI models,such as pruned neural networks or quantized models,can be designed for low-latencysignal detection and resource allocation.These models can quickly process the received signals and allocate re-sources(such as power or antennas)with minimal computational o

206、verhead.AI-based models can significantlyreduce the detection and resource allocation time in MIMO-IM systems,making them suitable for real-time ap-plications without sacrificing performance.Reinforcement Learning for Adaptive Antenna Configuration:The optimal configuration of antennas in MIMO-IM sy

207、stems may vary depending on the channel conditions,user mobility,and interference.Static or pre-definedantenna configurations may not always lead to optimal performance in dynamic environments.RL can be used toadaptively configure the antennas in real-time,based on feedback from the environment.An R

208、L agent can learnwhich antenna configurations maximize system performance under different conditions,dynamically adjusting theactive antenna indices to optimize throughput or minimize error rates.RL-based adaptive antenna configurationimproves the flexibility and adaptability of MIMO-IM systems,enab

209、ling them to maintain high performance inrapidly changing environments.AI-Assisted Error Correction:MIMO-IM systems can be prone to errors due to imperfect detection of both thetransmitted symbols and the active antenna indices.Traditional error correction methods,such as LDPC or Turbocodes,may not

210、be sufficient to handle the unique challenges posed by MIMO-IM.Deep learning-based errorcorrection models,such as neural decoders,can be trained to correct both symbol errors and index detection errorsin MIMO-IM systems.These models can learn from large datasets of transmitted and received signals t

211、o improvethe error correction process.AI-based error correction improves the reliability and robustness of MIMO-IMsystems,particularly in challenging channel conditions where traditional error correction may fail.The above-mentioned methods are compared in Table 4 in terms of the AI approach as well

212、 as the model size.Overall,generative AI and large models provide powerful tools to enhance the performance,efficiency,and adapt-ability of MIMO-IM systems.By leveraging AI for channel estimation,signal detection,resource allocation,anderror correction,MIMO-IM systems can achieve better spectral eff

213、iciency,lower latency,and higher reliability.AI-driven optimization of antenna selection and modulation strategies further improves the systems ability toadapt to dynamic environments,making it a key enabler for future communication technologies such as 5G andbeyond.2.2.6Joint ApproachesSometimes it

214、 is important to combine some physical functions together,for example joint symbol detection andchannel estimation,or joint equalization and decoding.By leveraging predictive modeling,data synthesis,andadaptive optimization,generative models provide better noise resilience,faster processing times,an

215、d improvedaccuracy,even in complex environments like massive MIMO systems and high-mobility scenarios.Heres howgenerative AI and large models can help in these areas:24Joint Symbol Detection and Channel Estimation:In wireless systems,symbol detection and channel estimationare interdependent processe

216、s.Traditional methods often handle these independently,which can lead to suboptimalperformance in noisy or high-interference environments.Generative AI models can simulate channel conditionsin advance,allowing for better symbol detection under varying noise levels and interference.They can synthesiz

217、edata to reduce the need for extensive pilot signals,allowing for better channel estimation with less overhead.Some example methods could be GANs or VAEs.Large neural networks(e.g.,RNNs)can jointly learn channelcharacteristics and symbols,enhancing detection reliability,especially in fading channels

218、.Overall,generative AIand large models can jointly optimize both,improving accuracy and efficiency.Table 4:Comparison of Deterministic vs.Generative ModelsApplicationDeterministic AIGenerative AITraining Model SizeInference Model SizeImproving channel estimationxxMedium to LargeMedium to LargeEnhanc

219、ing signal detectionxMedium to LargeSmall to MediumJoint optimization of antenna selec-tion and modulationxMedium to LargeSmall to MediumEnd-to-End Learning of MIMO-IMSystem ComponentsxxLargeLargeGenerative AI for Channel and In-terference SimulationxLargeMedium to LargeAI for Low-Latency Detection

220、andResource AllocationxxMedium to LargeSmallReinforcement Learning for Adap-tive Antenna ConfigurationxxMedium to LargeMediumAI-Assisted Error CorrectionxxMedium to LargeMedium to LargeJoint Equalization and Decoding:Joint equalization and decoding involve handling inter-symbol interference(ISI)and

221、correcting channel-induced distortions,particularly challenging in high-mobility and high-interferenceenvironments.Generative models can simulate interference scenarios,allowing adaptive equalization techniquesthat dynamically adjust to changes in interference.Deep learning models,such as CNNs,can j

222、ointly performequalization and decoding tasks,learning the structure of both interference and noise to improve decoding reli-ability.Finally,RL models can optimize equalization strategies in real-time,adapting to dynamic environmentsand improving decoding performance.In general,generative AI and lar

223、ge models offer predictive analytics andadaptive equalization techniques that improve decoding accuracy and speed.End-to-End Learning for Integrated Channel Estimation,Detection,and Decoding:End-to-end learning modelsuse neural networks to combine channel estimation,symbol detection,equalization,and

224、 decoding,creating anoptimized,single-model pipeline that adapts to complex and fast-changing environments.By jointly learning allsteps,generative models reduce processing time and computational complexity,ideal for real-time applications.This kind of models are adaptive,training across various sign

225、al,interference,and noise conditions,making themresilient to channel impairments and improving error rates.GANs are used to synthesize realistic channel effects,training models to better adapt to real-world interference and noise scenarios.2.2.7Wireless Spectrum SensingWireless standards continuousl

226、y evolve,leading to new ways to connect devices and a massive increase in con-necteddevices.ThisrapidexpansionpresentsasignificantchallengeregardingRFspectrumavailability.Spectrumsensing enables the detection of unused spectrum bands,known as spectrum holes.This allows secondary userswithout dedicat

227、ed licenses to access the spectrum not currently used by primary,licensed users.This opportunisticspectrum access enables more efficient spectrum usage by continuously monitoring the spectrum and identifyingspectrum holes.This technique is known as Dynamic Spectrum Access(DSA).Wireless Spectrum Sens

228、ing can be categorized into three main types:traditional methods such as energy detection,matched filtering,cyclo-stationary feature detection,and receiver metrics-based approaches;machine learning25and deep learning-based methods;and,more recently,large-scale AI approaches.Traditional spectrum sens

229、ing,particularly energy detection,involves comparing the computed signal energy against a threshold to determinethe presence or absence of a signal.However,this method is challenged by noise,interference,incomplete data,and environmental variability.These challenges make it difficult to set an optim

230、al threshold for detection.Whilepromising,machine learning and deep learning techniques struggle with generalizationmodels that perform wellon a specific data set fail to achieve similar accuracy on unseen data.Shao et al.9 propose a framework for adapting and enhancing LLMs for wireless communicati

231、on systems andsuggestsusingfew-shotlearningwithLLMsforspectrumsensing.Thistechniqueisimportantwhendeeplearningmodels require collecting large amounts of labeled data,which is difficult.With just a few examples,LLMs caneffectively learn the task and perform comparably to optimal detectors,such as ene

232、rgy detectors,especially inscenarios with varying signal-to-noise ratios.Traditional RF sensing methods face noise,interference,and incomplete data challenges.Wang et al.10 proposean RF sensing framework based on GenAI for IoT systems.GenAI enhances multi-modal data fusion,whichis essential for IoT

233、systems that depend on diverse sensor inputs,such as RF signals,images,and audio.Bycombining these different data types,GenAI-driven systems create more intelligent and comprehensive sensingsolutions.GenAI techniques,like GANs,VAEs,and Diffusion Models(DMs),can generate high-quality syntheticdata,de

234、-noise signals,and fill in the missing information.These abilities significantly strengthen the reliability ofRF sensing systems.Automatic Modulation Classification(AMC)can be used for wireless spectrum sensing by identifying the mod-ulation schemes of primary users(PUs).By classifying modulation sc

235、heme used by PUs,AMC enables wirelessradios to intelligently detect whether a spectrum band is occupied or available for secondary use,reducing inter-ference with licensed users.This integration of AMC in spectrum sensing system improves detection accuracy,especially in low Signal-to-Noise Ratio(SNR

236、)environments,compared to traditional methods like energy detec-tion.Olaloye et al.11 proposed the use of machine learning models,such as Multi-Layer Perceptron(MLP)and have demonstrated high accuracy in classifying modulation types.Therefore,validated the use of AMC inreal-time DSA.2.3Large-Scale A

237、I for Network Management and Optimization2.3.1Large-Scale AI in User-centric Network OptimizationUser-centric Network Optimization has become a focal point in next-generation network optimization.This ap-proach is crucial because it addresses the diverse needs and preferences of individual users,lea

238、ding to improvedoverall QoE.Traditional uniform service delivery often results in varying levels of user satisfaction.User-centricoptimization can be applied in various scenarios,such as personalized content delivery,adaptive video stream-ing,and dynamic resource allocation in mobile networks.Despit

239、e the emergence of several User-centric NetworkOptimizationmethods,accuratelyassessinguser requirements,particularly subjective experiences,remains achal-lenge.Some studies have incorporated psychological laws to approximate users subjective QoE 12.However,these approaches often fail to capture the

240、complexity of real-world applications.An alternative solution involvesusing Reinforcement Learning with Human Feedback(RLHF)paradigms to train management models.Thismethod requires ongoing QoE feedback from experts,which is expensive,raises ethical concerns,and is difficultto implement in real time.

241、These limitations lead to our first research question:Large-scale AI presents significant potential in User-centric Network Optimization due to its ability to process vastamounts of user data during training,enabling it to simulate user QoE effectively.LLMs-empowered generativeagents can process and

242、 understand complex instructions in natural language,serving as a universal interface for26various tasks,including evaluation 13,14.Research in 13 demonstrates ChatGPTs capacity to evaluate textualcontent across human-aware criteria such as quality,tone,and coherence.These evaluations lay the ground

243、workfor extending LLM functionality to other domains.Further studies in 15 assess the potential of LLMs likeChatGPT in Computational Social Science,examining their performance in classification and generative tasks ina zero-shot manner.Results indicate that LLMs show fair agreement with humans and c

244、an enhance the annotationprocess.Recent work in 16 reveals that modern role-playing LLMs can effectively mimic specific personalitytraits,achieving an 82.8%alignment with human perceptions.In the context of Large-Scale AI in User-centricNetwork Optimization,AI can serve two primary roles:Active Solu

245、tion Generation:Large-scale AI can actively generate network optimization solutions.Scalablemodel architectures suitable for decision-making include transformer-based models with attention mecha-nisms,graph neural networks for network topology understanding,and hierarchical reinforcement learningmod

246、els for multi-level decision processes.For example,the authors in 17 propose an innovative LLM-enabled Mixture of Experts(MoE)approach for network optimization.This method leverages the LLMsadvanced reasoning capabilities to analyze user objectives and constraints,select specialized DRL experts,and

247、determine the decision weights for each participating expert.The LLM acts as a dynamic gate network,managing the selection and integration of expert models to address new and complex optimization tasks.This approach demonstrates the potential of large-scale AI in adapting to diverse user requirement

248、s andgenerating effective solutions for network optimization problems without the need to train new models foreach specific task.Passive Optimization Support:Large-scale AI can function as a component of optimization algorithms,providing subjective QoE assessments.LLM-empowered generative agents off

249、er a powerful mechanismto provide human-aware subjective QoE feedback for generated content.A QoE feedback scheme usingthese agents can simulate diverse user personalities.By utilizing prompts and assigning one agent per user,generative agents can mimic users with varied subjective preferences,deliv

250、ering evaluations of receivedservices.For example,the authors in 18 propose a Reinforcement Learning with LLMs Interaction(RLLI)framework for distributed GenAI services.This approach leverages LLM-empowered generative agents tosimulate user feedback.The framework uses the Big Five personality model

251、as a basis for configuringgenerative agents,aligning with research showing that LLMs can effectively simulate these personalitytraits.By designing prompts that include specific Big Five trait scores,the system enables generative agentsto mimic diverse user personalities.These agents then evaluate ge

252、nerated content,providing subjectiveQoE feedback that reflects individual preferences.This method offers a scalable and efficient alternative tohuman feedback,demonstrating improved performance in maximizing sum QoE compared to conventionalmethods.2.4Large-Scale AI for Un-crewed Aerial Vehicles(UAVs

253、)Un-crewed aerial vehicles(UAVs)have recently gained significant attention due to their exceptional autonomy,mobility,and adaptability.These attributes have expanded their use across a broad spectrum of applications,in-cludingsurveillance,searchandrescuemissions,healthcare,andmaritimecommunications1

254、9.Theconvergenceof advancements in UAV technology and AI has yielded significant benefits across a wide range of applications.For instance,AI-enabled UAVs utilize facial recognition to enhance security applications,and real-time videoanalysis enables monitoring remote areas.In agriculture,UAVs equip

255、ped with AI models assess crop health,en-abling precision farming that increases revenues.Additionally,AI-driven UAVs optimize logistics by enhancingroute planning and inventory management,thereby streamlining warehouse operations and increasing delivery ef-ficiency 20,21.Among these advancements,la

256、rge-scale AI models have recently attracted considerable attentionin the UAV sector 22.The capabilities of these models in real-time data processing,natural language understand-27ing and generation,content recommendation,sentiment analysis,automated response,language translation,andcontent summarizi

257、ng have paved the way for new opportunities within the UAV domain.Recent literature 23,24,25,26 has investigated the integration of large-scale AI models into UAV communica-tion systems to enhance interaction between human operators and UAVs,as well as among the UAVs themselves.Traditionally,UAVs ha

258、ve relied on pre-programmed commands,offering limited dynamic interaction capabilities.However,the incorporation of such large-scale AI models;i.e.,LLMs,introduces support for natural and intuitivecommunication methods.For instance,LLMs can interpret and respond to commands in natural language,mak-i

259、ng UAV control more straightforward and enabling the management of complex,real-time mission adjustments.This evolution transforms UAVs into more adaptable and practical tools across a wide range of applications.Forexample,in 27 the authors provided a framework that utilizes GPT-3 to enhance the int

260、uition of human-UAVinteractions.The framework leverages NLP techniques to allow users to control UAVs using simple languagecommands,eliminating the need for complex programming knowledge.By translating user instructions intoexecutable code,such a framework enables UAVs to carry out tasks and provide

261、 feedback in natural language,sig-nificantly simplifying the control process.Another application is provided in 28,in which the authors presenteda framework that integrates OpenAIs GPT-3.5-Turbo model with an UAV simulation systems(i.e,PX4/Gazebosimulator),to develop a natural language-based drone c

262、ontrol system.The systems architecture is designed tofacilitate seamless interaction between the user and the UAV simulator through a chatbot interface,enabled by aPython-based middleware.This middleware processes natural language inputs from the user,relays them to theChatGPT model using the OpenAI

263、 API,retrieves the generated responses,and translates them into commands thatthe simulator can interpret,thereby enhancing the interactivity and accessibility of the UAV simulation system.Large-scale AI models enable UAVs to react instantly to dynamic environmental changes and communicationdemands.T

264、he adaptive learning capabilities of such models enable continuous improvement in operational strate-gies by leveraging incoming data,thereby enhancing decision-making processes.In 27,the authors introduceda vision-based autonomous planning system for UAVs designed to enhance safety.The system predi

265、cts the tra-jectories of dynamic obstacles and generates safer flight paths by utilizing NanoDet for precise obstacle detectionand Kalman Filtering for accurate motion estimation.In another work 29,the authors integrated GPT modelsand computer vision technologies into autonomous inspection UAVs to e

266、nhance their functionality in indoor en-vironments.The proposed system enables UAVs to analyze images captured during flight to generate detailedobject dictionaries.These dictionaries enable the UAVs to recognize and understand various elements within theirenvironment,allowing them to dynamically ad

267、apt their behavior in response to both anticipated and unforeseenconditions.Additionally,large-scale AI models can enhance UAVs autonomous decision-making by leveraging communica-tion context or environmental data 30.For instance,during a search and rescue operation,live video feeds andtext reports

268、from multiple UAVs can be analyzed and synthesized using multi-model LLMs to recommend areas offocus or adjust search patterns accordingly 23.UAVs can also operate in ad-hoc and mesh configurations to formdynamic networks without the need for pre-existing infrastructure.This capability is especially

269、 valuable in situa-tions where establishing permanent network infrastructure is impractical,such as in disaster response.Such selfmade networks continuously discover new neighbors and can dynamically adjust routes based on the networkstopology and traffic conditions,thereby improving scalability and

270、 flexibility 31.Large-scale AI also contribute to simulating and modeling the behavior of networks under different scenarios,aiding in the planning and decision-making processes for UAV deployments.The GPT series can simulate variouscommunication scenarios for UAV training by generating realistic mi

271、ssion scenarios and responses.This allowsoperators to undergo comprehensive training,equipping them to handle different situations more effectively andenhancing their preparedness for real-world operations 32.Large-scale AI models can also assist UAVs in28understanding network traffic patterns,enabl

272、ing them to recommend adaptive protocols that reduce latency andincrease throughput,especially under the varying user conditions often encountered in these networks.Large-scale AI models can be also utilized to analyze data from the UAVs themselves,including operational logsand flight data,to predic

273、t possible failures,maintenance needs,and potential malicious attacks,before they happen33.This predictive capability can significantly enhance the reliability and lifespan of UAVs,thereby reducingdowntime and maintenance costs.In 34,the authors developed enhanced security and forensic analysis prot

274、ocolsfor UAVs to support the growing use of drones across various sectors,including those at risk of criminal misuse.They introduced a named entity recognition system to extract information from drone flight logs.This systememploys fine-tuned BERT and DistilBERT models with annotated data,significan

275、tly improving the identificationof relevant entities essential for forensic investigations of drone-related incidents.2.5Large-Scale AI for Telecom Use Cases2.5.1”Qiming”Network Large Model Case StudyChina Telecoms”Qiming”Network Large Model is designed to optimize and automate network operationsthr

276、ough AI-driven processes.As modern telecommunications networks become increasingly complex,the needfor advanced tools capable of real-time decision-making has grown.The”Qiming”model leverages vast amountsof data and professional network knowledge to assist in various tasks such as network planning,m

277、aintenance,monitoring,troubleshooting,and performance optimization.The models generative capabilities,combined withknowledge retrieval and intent recognition,aim to enhance network autonomy,reduce manual intervention,andimprove operational efficiency.The”Qiming”Network Large Model employs innovative

278、 large model architectures,including incremental train-ing and feedback optimization algorithms.These ensure that the model evolves over time to adapt to new networkchallenges and requirements.It is also capable of handling vast amounts of network data,significantly improv-ing operational efficiency

279、 and reducing the need for manual input.Despite these advantages,the model faceschallenges such as the high computational cost associated with processing large datasets and training the model.Moreover,the model may struggle with generalization when encountering entirely new or unforeseen networkcond

280、itions,which requires ongoing optimization and updates.The”Qiming”Network Large Model exemplifieshow large-scale AI can be leveraged to tackle complex network management challenges.It serves as a vital tool forChina Telecom,driving the companys network automation efforts and enhancing the efficiency

281、 of its operations.2.5.2”Qiming”Network Large Model Operational WorkflowThe workflow of the”Qiming”model,as illustrated in the accompanying diagram,outlines a multi-step process:1.User Intent and Input:Network operation and maintenance staff initiate the process by providing a specific userintent,su

282、ch as a request for network optimization or troubleshooting.2.Querying Network Knowledge:The Network Large Model interacts with the knowledge base by querying forrelevant network knowledge.This step includes retrieving professional knowledge that assists in decision-makingprocesses such as network o

283、ptimization or answering user queries.3.Querying Network Data:The model queries network data from databases and other sources,distinguishingbetween real-time and non-real-time data.This data could include statistics and network status,which is crucialfor diagnosing issues and providing accurate reco

284、mmendations.The interaction with the BSS(Business SupportSystem),OSS(Operational Support System),and MSS(Management Support System)allows for the extractionof operational data and relevant metrics for a comprehensive analysis.294.Decision-Making:Based on the gathered knowledge and data,the model mak

285、es decisions that pertain to thenetwork operation.This could involve generating answers,recommending solutions,or optimizing network func-tions.Network Operation Response:The Network Large Model then dispatches the decision back to the staffor directly interacts with the network components.The netwo

286、rk operates based on the models decisions,and anacknowledgment(ACK)is sent back,confirming the successful implementation of the operation.Figure 2:”Qiming”Network Large Model Operational Workflow2.5.3”Qiming”Network Large Model Application ScenariosIntelligent Network Operations:The model enables th

287、e automation of network tasks across the entire lifecy-cle,from planning and construction to maintenance and optimization.By using advanced algorithms,it ensuresefficient,real-time responses to network issues.Fault Diagnosis and Prevention:The models ability to process both historical and real-time

288、data allows it topredict potential network failures and provide preventive measures.This helps reduce downtime and ensures asmooth network experience for users.Task Decomposition and Orchestration:The model can break down complex tasks into manageable steps,providing intelligent task orchestration.T

289、his capability improves the speed and accuracy of network maintenanceand troubleshooting.30AI THEORY OF LARGE TELECOM MODELS3AI Theory of Large Telecom Models3.1From Language to Telecom Models:Challenges and Necessary ModificationsWhile state-of-the-art LLMs excel in versatile NLP tasks like questio

290、n answering and sentence completion,en-suring similar performance upon integrating them into telecoms requires further modifications to their underlyingtheory and mechanisms.For instance,simply relying on a GPT architecture in dealing with network parame-ters and KPIs such as SNR,QoS,and channel gai

291、ns can lead to erroneous mistakes.This is mainly due to thelimitations inherited from text-based models which are transferred to telecom models,leading to the followingdrawbacks:Limited abstract telecom knowledge:The attention mechanism that perfectly captures the sophisticatedcorrelations between t

292、okens(or generally words)falls short in capturing the other relations(i.e.,causal,mathematical,etc.)that govern such telecom tokens.In fact,LLMs build their own knowledge that maynot necessarily reflect the real-world phenomenon.For instance,an LLM may not properly understand thecausal relation betw

293、een increasing the transmission frequency and the elevated propagation losses encoun-tered by a wireless signal.Clearly,one of the most prominent implications of this limited knowledge is thetendency of large models to hallucinate when generating their repose.Lack of mathematical foundations:In gene

294、ral,LLMs build on their captured patterns to define the mathe-matical operations that govern the different telecom tokens.Hence,they lack the proper mathematical foun-dations that enable them to freely manipulate and verify the tokens.For instance,LLMs may struggle toprove how the Rx antenna measure

295、ments(e.g.,Reference Signal Received Power(RSRP)mathematicallyflow from the underlying theorems and equations of wireless signal propagation(e.g.,pathloss).Accord-ingly,LLMs cannot calculate how the captured parameters can be proven based on the channel informationand transmit signals.This can poten

296、tially hinder the validity of the LLM results in different situations orlimit the applications of LLMs in situations that require reasoning and planning.A simple example of thiscan be in the form of a network design problem.Although an LLM can elaborate on the design questionsthat relate power at th

297、e Tx antenna and the pathloss,it it may not be able to capture that doubling the propa-gation distance would actually decrease the power at the Rx by a factor of 4.Thus,state-of-art LLMs cannotfully apprehend the telecom formulations behind these telecom tokens.Static performance:As LLMs are trained

298、 on massive datasets up to a certain point in time,they showa static performance that may become non-relevant when it comes to dynamic and non-stationary settingssuch as those introduced in the RAN.Unlike text and generally language that is mostly static,a dynamicenvironment such as the RAN demands

299、telecom models to admit evolving knowledge paradigms.Absenceofguardrails:UnlikemostLLMsthatcanbeusedtoboostproductivityandenhanceperformance,the role of LLMs in telecoms may demand autonomously taking critical decisions that drive the networkoperations.To this end,these actions must adhere to specif

300、ic rules and abide by the guidelines set byregulatory bodies(e.g.,FCC,ETSI,etc.).Nevertheless,the state-of-art LLMs do not impose any guardrailsin their design.For instance,an LLM may not set the transmit power of a base station above a predeterminedthreshold as it may threaten to harm individuals.T

301、o address these drawbacks,large telecom models must consider necessary modifications into their foundationallanguage architectures before being implemented in the telecoms sector.Next,we shed light on emerging AIapproaches,such as causality and neurosymbolic AI,that can potentially fill in the afore

302、mentioned gaps in the AItheory behind large telecom models.313.1.1Grounding via causalityFirst,large telecom models must enable the grounding of their telecom tokens so that they harness true meaningand acquire full understanding abilities about their data.Here,grounding is the process of anchoring

303、the generatedresponses of these models into real-world knowledge.In the telecom terms,it is the ability of large telecom modelsto root the telecom tokens(or embedded representations)into the physical world and wireless phenomenon.Thisensures that large telecom models maintain coherence to the real-w

304、orld context and true physical phenomenon.Effectively,this takes place by integrating the absent logical mechanisms to complement their knowledge gauge.As denoted earlier,these models may lack to capture the causal dimensions between the tokens.Hence,onemethod to enable grounding can be through the

305、framework of causal reasoning.This can facilitate a level ofcausal understanding that refers to identifying cause-and-effect relationships among various features within wire-less tokens 35.For instance,a vector of channel measurements in a wireless environment can be interpretedusing a causal graph

306、that identifies the relationships among scattering objects and multipath characteristics suchas angle of arrival,delay,and path gains 36.In particular,causal discovery methods 36 can be leveraged toidentify the cause and effect relations among the network variables that can further be arranged as st

307、ructural causalmodels.As it may be challenging to extract the causal variables and identities when dealing with high-dimensionalobservations(e.g.,Rx antenna measurements),causal representation learning presents an effective solution to mapthese observations into low-dimensional representations that

308、capture only the relevant causal variables.In fact,the captured representations could be further clustered on the basis of their similarity into general representa-tions.Consequently,this can reduce the embeddings space while still ensuring that distinct representations remaindifferentiable from eac

309、h other.Henceforth,causal discovery and causal representation learning enable can filterthe high-dimensional wireless observations to a minimally compact and sufficient embedding space suitable forlarge scale telecom models with evolving knowledge.Furthermore,telecom models could additionally bolste

310、rtheir performance through identifying the relevant causal variables from a telecom specific dataset.In particular,leveraging RAG can equip large telecom models with the necessary wireless and telecom knowledge.As shownin Fig.3,adopting RAG from a telecom specific source dataset can enable bolster t

311、he performance of telecommodels.3.1.2Alignment via RAGAlignment ensures that the outputs of large telecom models align with the guidelines of MNO.In particular,these guidelines should comply with government regulations and adhere to the goals of system designers.Hence,telecommodelsmustensurethatthey

312、achievetheseguidelineswithoutcontradictingtheirinitialpremiseandrefinetheir available settings to abide by the guardrails.For instance,while various complex modulation schemes andbeamforming principles can be discussed in telecom literature,supporting such approaches might not necessarilyalign with

313、the limited set of transmission schemes approved by 3GPP standards.Therefore,it is essential toensure that telecom models adhere to the standards and regulations.Furthermore,3GPP standards are updatedperiodically.Hence,it is crucial that telecom models remain consistent with these updates.One notabl

314、e approachto keep large telecom models up to date with standards is to leverage RAG with a dynamically evolving databaseto extract relevant contextual information for the wireless tasks enabled by the foundation model.Moreover,thisalignment requires an adopting an RL with wireless feedback mechanism

315、 that ensures that the responses fromthe telecom model maximizes the average reward.Here,the reward can be defined as proportional to the QoE ofnetwork agents.Notably,establishing this dynamically evolving database requires concerted efforts from industry,academia,and standardization bodies.32Figure

316、 3:A sample mathematical Q/A pair from the wireless specific dataset 37.3.1.3Dynamic performance via instructibilityAdditionally,LLMs struggle to operate in real-time environments and lack adaptability to changing wireless con-ditions and tasks.To address this,large telecom models must incorporate i

317、nstructibility,enabling them to adjusttheir parameters and behavior in response to evolving environments and tasks.3.1.4Neuro-symbolic AI as a cornerstone for mathematical reasoningRecent advances in LLMs focus on scaling AI models to enhance generalization capabilities.While the humanbrain requires

318、 only a few symbolic rules and experiences to generalize behavior to unseen scenarios,LLMs needtrillions of parameters to acquire knowledge for generalization.Despite this massive scale,they often fail toperform deductive reasoning,making them vulnerable to extreme or uncommon scenarios.Inspired by

319、humanintelligence,a promising approach is to build a hybrid system that combines the best of both worlds:a symboliccomponent that represents rule-based logic and background knowledge,enabling logical reasoning,and a neuralcomponent that allows for generalization of their behavior under epistemic unc

320、ertainty.Such hybrid models arecalled neurosymbolic AI.Neurosymbolic AI models allow to build sample efficient telecom models.Moreover,they help to build instructible wireless sytems,wherein their parameters can be dynamically adapted in responseto the environment or user feedback.As highlighted in

321、38,next-generation AI models for telecom must exhibit long-term planning capabilities.Here,planning refers to the capability of network components to propose a sequence of actionsencompassing bothnetwork configurations and actions of connected autonomous agentsby predicting future environmental stat

322、es.Such planning must ensure that connected agents maintain a high quality of experience while satisfying networkintent without interruption.These capabilities enable networks to configure actions such as beamforming andpower allocation,alongside autonomous agent control policies,ensuring that the q

323、uality of experience for net-work agents(involves both autonomous agents part of cyber-physical-systems and the network infrastructure)ismaximized while satisfying the network intent without interruption.Here,intent refers to specific goals that the33network must achieve,such as maximizing the netwo

324、rks sustainability.Herein,traditional AI methods such asdeep reinforcement learning is not sufficient to perform real-time control and network actions,due to the over-head in retraining.Moreover,such data-driven AI models are not trustworthy,and hence their decisions cannot betrusted as we move towa

325、rds building autonomous networks.Herein,causal inference enables performing interven-tions and counterfactuals 36 on the learned causal world model,that describes the interactions of network withthe autonomous agents.Interventions and counterfactuals 36 enable analyzing the impact of network actions

326、 onquality of experience of network agents.Using such effect analysis,network can compute optimal actions thatremediate any deviations from expected quality of experience for the network agents.One promising approach to instill mathematical reasoning is through invertible symbolic regressions 39 tha

327、tlearns underlying equations that describes the physical processes from the data.Such symbolic equation learnerscan be used to learn non-linear mathematical equations using symbolic expressions that cannot be described usingmodel-based systems.Such symbolic expressions enhance the explainability of

328、the AI models compared to usingblack-box models,that lack interpretability.3.2On the Interplay between Data Compression and LLMsThe connections between large language models and data compression operates in two complementary directions.On the one hand,the principles of compression are inherently rel

329、evant to the design and operation of languagemodels neural networks.However,deploying LLMs in resource-constrained scenarios requires applying compres-sion techniques to meet performance and infrastructure requirements.LLMs and data compression share a fundamental goal:reducing redundancy while reta

330、ining meaningful informa-tion.As shown in recent work 40,optimizing the conditional probability of the next token in language modelsis similar to the principle of arithmetic(source)coding to minimize the average coding length.This equivalencehighlights that language models,through their next-token p

331、rediction objective,inherently perform a form of com-pression.This natural alignment suggests that the internal mechanisms of LLMs can be fine-tuned to improve both theirpredictive accuracy and efficiency.Techniques such as quantization and pruning,which traditionally belong tothe field of data comp

332、ression,can be applied to LLMs without compromising their performance.The interplaybetween data compression and LLMs not only improves inference efficiency but also creates opportunities fordeploying lightweight models in dynamical telecom environments.The deployment of LLMs in wireless networks whe

333、re latency,memory,and energy constraints are critical re-quires signification model compression.Without compression,the size and computational demands of LLMspose significant challenges for real-time operations at the edge.Quantization reduces the bit-width used to represent weights and biases from standard 32-bit floating-point tolower-precision formats,such as 8-bit or 4-bit.It comes in two prim