1、6G Near-Field TechnologiesWhite Paper 2.0ConsultantsTiejun Cui(),Southeast UniversityPing Zhang(),Beijing University of Postsand TelecommunicationsXiaohu You(),Southeast UniversityYonina Eldar(yonina.eldarweizmann.ac.il),WeizmannInstitute of ScienceJiangzhou Wang(),Southeast UniversityJiying Xiang()

2、,ZTE CorporationEditors in ChiefYajun Zhao(),ZTE CorporationLinglong Dai(),Tsinghua UniversityJianhua Zhang(),Beijing University ofPosts and TelecommunicationsLong Li(),Xidian UniversityChapter EditorsChongwen Huang(),ZhejiangUniversityYuanwei Liu(yuanweihku.hk),The University of HongKongYifei Yuan(

3、),China MobileShuang Zheng(),ZTE CorporationContributors(*Order by the first letter of names.)Ahmed Al Hammadi(ahmed.alhammaditii.ae),TechnologyInnovation InstituteAhmed Elzanaty(a.elzanatysurrey.ac.uk),University ofSurreyAng Chen(),University ofScience and Technology of ChinaAnton Tishchenko(a.tish

4、chenkosurrey.ac.uk),5G/6GInnovation Centre,University of SurreyAshwin Thelappilly Joy(qq566jdsurrey.ac.uk),5G/6GInnovation Centre,University of SurreyBeixiong Zheng(),South ChinaUniversity of TechnologyBo Ai(),Beijing Jiaotong UniversityBohao Wang(),Zhejiang UniversityBokai Xu(),Beijing JiaotongUniv

5、ersityBoqun Zhao(boqun1ualberta.ca),University of AlbertaChan-Byoung Chae(cbchaeyonsei.ac.kr),Yonsei UniversityChangsheng You(),SouthernUniversity of Science and TechnologyChao Feng(chao_),Southeast UniversityChao Zhou(),SouthernUniversity of Science and TechnologyChau Yuen(chau.yuenntu.edu.sg),Nany

6、ang TechnologicalUniversityChen Sun(),SONY China Research LabChen Yang(),Beijing Institute ofTechnology(Zhuhai)Chenhao Qi(),Southeast UniversityChenxi Zhu(),Lenovo ResearchChong Han(),Shanghai Jiao TongUniversityChong Huang(chong.huangieee.org),Sun Yat-senUniversityChongjun Ouyang(c.ouyangqmul.ac.uk

7、),Queen MaryUniversity of LondonChongwen Huang(),ZhejiangUniversityCong Zhou(),Harbin Institute ofTechnology/Southern University of Science and TechnologyCunhua Pan(),Southeast UniversityDajie Jiang(),vivo Software TechnologyCo.Ltd.Daniel Benevides da Costa(danielbcostaieee.org),KingFahd University

8、of Petroleum&MineralsDavide Dardari(davide.dardariunibo.it),University ofBolognaDeyue Zou(),Dalian University ofTechnologyDi Zhang(dr.di.zhangieee.org),Zhengzhou UniversityDongxuan He(dongxuan_),Beijing Institute ofTechnologyDu Pan(),China Mobile GroupDesign Institute Co.,Ltd.Ehsan Tohidi(tohiditu-b

9、erlin.de),Technische UniversittBerlinEmil Bjrnson(emilbjokth.se),KTH Royal Institute ofTechnologyFambirai Takawira(Fambirai.takawirawits.ac.za),University of WitwatersrandFan Liu(),Southeast UniversityFan Wang(wangfdocomolabs-),DOCOMOBeijing Communications Laboratories Co.Ltd.Fan Zhang(),Tsinghua Un

10、iversityFang Fang(fang.fanguwo.ca),Western UniversityFang Yang(),Tsinghua UniversityFaouzi Bader(carlos-faouzi.badertii.ae),TechnologyInnovation InstituteFeifei Gao(feifeigaoieee.org),Tsinghua UniversityFeng Shu(),Hainan UniversityGabriele Gradoni(g.gradonisurrey.ac.uk),5G/6G InnovationCentre,Univer

11、sity of SurreyGang Yang(),University ofElectronic Science and Technology of ChinaGaojian Huang(),Henan PolytechnicUniversityGaojie Chen(gaojie.chenieee.org),Sun Yat-sen UniversityGaurav Bhargava(),Nationalinstitute of Technology MeghalayaGong Tierui(tierui.gongntu.edu.sg),Nanyang TechnologicalUniver

12、sityGuiping Lu(),Beijing Institute ofTechnology(Zhuhai)Haifan Yin(),Huazhong University ofScience and TechnologyHaiquan Lu(),Southeast UniversityHaixia Liu(),Xidian UniversityHaiyang Miao(),Beijing University ofPosts and TelecommunicationsHaiyang Zhang(),NanjingUniversity of Posts and Telecommunicat

13、ionsHamidreza Tagvahee(h.taghvaeesurrey.ac.uk),5G/6GInnovation Centre,University of SurreyHao Jiang(),Nanjing University ofInformation Science and Technology.Hao Lei(),Beijing Jiaotong UniversityHao Qin(hao.qinucdconnect.ie),University College DublinHao Xue(),Xidian UniversityHaodong Zhang(),Souther

14、n University of Science and TechnologyHaris Pervaiz(haris.pervaizessex.ac.uk),University of EssexHong Ren(),Southeast UniversityHongbo Xing(),Beijing University ofPosts and TelecommunicationsHongkang Yu(),ZTE CorporationHongliang Luo(),TsinghuaUniversityHongqiang Cheng(),Southern University of Scien

15、ce and TechnologyHongxia Miao(hongxia_),BeijingUniversity of Posts and TelecommunicationsHuizhi Wang(),SoutheastUniversityJalil Kazim(Jalil.Kazimglasgow.ac.uk),University ofGlasgowJi Wang(),Central China NormalUniversityJiachen Tian(),Southeast UniversityJian Song(),Tsinghua UniversityJianchi Zhu(),

16、China TelecomResearch InstituteJiangzhou Wang(),Southeast UniversityJianhua Zhang(),Beijing University ofPosts and TelecommunicationsJianwu Dou(),ZTE CorporationJiao Wu(jiao.wukaust.edu.sa),King Abdullah University ofScience and TechnologyJiapeng Li(),SouthernUniversity of Science and TechnologyJiaq

17、i Han(),Xidian UniversityJiaqi Xu(jiaqi.xuqmul.ac.uk),Queen Mary University ofLondonJiaxue Li(jiaxue_),ZhengzhouUniversity/ChinaJiayi Zhang(),Beijing JiaotongUniversityJiayu Shen(),Zhejiang UniversityJie Hu(),University of Electronic Scienceand Technology of ChinaJie Feng(),South China Universityof

18、TechnologyJiguang He(jiguang.hetii.ae),Technology InnovationInstituteJintao Wang(),TsinghuaUniversityJingqing Wang(),Xidian UniversityJosep Miquel Jornet(j.jornetnortheastern.edu),NortheasternUniversityJun Du(),Tsinghua UniversityJun Liu(),University of ElectronicScience and Technology of ChinaKai-K

19、it Wong(kai-kit.wongac.ucl.uk),University CollegeLondonKangda Zhi(k.zhitu-berlin.de),Technische UniversittBerlinKangjian Chen(),Southeast UniversityKun Yang(),vivo SoftwareTechnology Co.Ltd.Kun Yang(),Nanjing UniversityLexi Xu(),China Unicom&BeijingUniversity of Posts and TelecommunicationsLeyi Zhan

20、g(),ZTE CorporationLi Chen(),University of Science andTechnology of ChinaLijuan Dong(donglijuan_),Shanxi DatongUniversityLinglong Dai(),Tsinghua UniversityLipeng Zhu(zhulpnus.edu.sg),National University ofSingaporeLiyang Lu(),TsinghuaUniversityLong Li(),Xidian UniversityLong Zhang(),Sun Yat-sen Univ

21、ersityLulu Song(lulu_),Zhengzhou UniversityMarco DI RENZO(marco.di-renzouniversite-paris-saclay.fr),Paris-Saclay UniversityMengnan Jian(),ZTE CorporationMrouane Debbah(merouane.debbahku.ac.ae),KhalifaUniversity of Science and Technology&CentraleSupelec,University Paris-SaclayMiaowen Wen(),South Chin

22、aUniversity of TechnologyMingyao Cui(cuimy23connect.hku.hk),The University ofHong KongMingju Li(),XiaomiMingyao Cui(cui-),TsinghuaUniversityMohsen Khalily(m.khalilysurrey.ac.uk),5G/6G InnovationCentre,University of SurreyMuhammad Ali Imran(Muhammad.lmranglasgow.ac.uk),University of GlasgowNan Zhang(

23、),ZTE CorporationNan Zhao(),Dalian University ofTechnologyNanxi Li(),China TelecomNavneet Agrawal(navneet.agrawaltu-berlin.de),TechnischeUniversitt BerlinNeel Kanth Kundu(neelkanthiitd.ac.in),Indian Institute ofTechnology Delhizlem Tugfe Demir(ozlemtdkth.se),KTH Royal Institute ofTechnologyPan Tang(

24、),Beijing University ofPosts and TelecommunicationsPengfei Wang(),vivo SoftwareTechnology Co.Ltd.Qammer H.Abbasi(Qammer.abbasiglasgow.ac.uk),University of GlasgowQi Gu(),China MobileQi Zhang(),ZTE CorporationQiang Feng(),Xidian UniversityQingqing Wu(),Shanghai Jiao TongUniversityQingxiao Huang(),Uni

25、versity ofElectronic Science and Technology of ChinaQiuyan Liu(),China UnicomQu Luo(q.u.luosurrey.ac.uk),University of SurreyQurrat-Ul-Ain Nadeem(qurrat.nadeemnyu.edu),New YorkUniversity(NYU)Abu Dhabi and NYU TandonRan Ji(),Zhejiang UniversityRitao Cheng(),ChinaMobile Group Design Institute Co.,Ltd.

26、Robert W.Heath Jr(rwheathjrucsd.edu),University ofCaliforniaRui Zhang(),The Chinese University ofHong Kong,Shenzhen/National University of SingaporeRuiqi(Richie)Liu(),ZTE CorporationRuirui Sun(),Southeast UniversitySaber Hassouna(Saber.Hassounaglasgow.ac.uk),Universityof GlasgowSeungnyun Kim(snkim94

27、mit.edu),Massachusetts Instituteof TechnologyShan Wang(),ZTE CorporationShaohua Yue(),Peking UniversityShengheng Liu(),Southeast UniversityShi Jin(),Southeast UniversityShicong Liu(sc.liumy.cityu.edu.hk),City University of HongKongShihang Lu(),SouthernUniversity of Science and TechnologyShiru Duan()

28、,Beijing InformationScience and Technology UniversityShuai S.A.Yuan(),ZhejiangUniversityShuang Zheng(),ZTE CorporationShubhankar Majumdar(),Nationalinstitute of Technology MeghalayaShuhao Zeng(),PrincetonUniversityShupei Zhang(),Peking UniversitySicong Liu(),Xiamen UniversitySawomir Staczak(slawomir

29、.stanczaktu-berlin.de),Technische Universitt BerlinSongtao Gao(),ChinaMobile Group Design Institute Co.,Ltd.Syed Tariq Shah(syed.shahessex.ac.uk),University of EssexTahsin Akalin(Tahsin.Akalinuniv-lille.fr),Matre deConfrences(European Microwave Lecturer)Tianqi Mao(),Beijing Institute ofTechnologyTia

30、ntian Ma(),South ChinaUniversity of TechnologyTianwei Hou(),Beijing JiaotongUniversityTianyu Liu(),SouthernUniversity of Science and TechnologyTingting Fan(Emme.F),SONY China ResearchLabVictor Monzon Baeza(vmonzonuoc.edu),UniversitatOberta de CatalunyaWahab Almuhtadi(almuhtadiieee.org),Algonquin Col

31、legeWenchi Cheng(),Xidian UniversityWang Liu(w_),Southeast UniversityWanming Hao(),Zhengzhou UniversityWei Ci(),Southeast UniversityWei E.I.Sha(),Zhejiang UniversityWei Li(l_weintu.edu.sg),Nanyang TechnologicalUniversityWei Xi(),XiaomiWeidong Hu(),Beijing Institute ofTechnologyWeidong Li(),Huazhong

32、University ofScience and TechnologyWeihua Yu(),Beijing Institute ofTechnology/BIT Chongqing Institute of Microelectronics andMicrosystemsWenhui Yi(),Beijing JiaotongUniversityWenjie Hou(),Beijing Institute ofTechnology(Zhuhai)Wenyan Ma(wenyanu.nus.edu),National University ofSingaporeWentao Yu(wyuaqc

33、onnect.ust.hk),The Hong KongUniversity of Science and TechnologyWei Zhang(w.zhangunsw.edu.au),University of New SouthWalesXiang Li(lixdocomolabs-),DOCOMOBeijing LabsXianghao Yu(alex.yucityu.edu.hk),City University of HongKongXianjun Yang(),CICT MobileCommunication Technology Co.,Ltd.Xiaolin Hou(houd

34、ocomolabs-),DOCOMOBeijing LabsXiaowen Tian(xtian8ncsu.edu),North Carolina StateUniversityXidong Mu(x.muqub.ac.uk),Queens University BelfastXin Su(),China MobileXin Su(),CICT MobileCommunication Technology Co.,Ltd.Xin Wang(),Xidian UniversityXing Zhang(xing_),Nanjing Universityof Posts and Telecommun

35、icationsXingqi Zhang(xingqi.zhangualberta.ca),University ofAlbertaXingwang Li(),Henen PolytechnicUniversityXinrui Li(xinrui_),Southeast UniversityXinwei Yue(),Beijing InformationScience and Technology UniversityXinyu Xie(),Shanghai Jiao TongUniversityXiyuan Liu(),Tongji UniversityXu Gan(gan_),Zhejia

36、ng UniversityXu Shi(shi-),Tsinghua UniversityXue Xiong(),South ChinaUniversity of TechnologyXuehua Li(),Beijing InformationScience and Technology UniversityXuesong Cai(),Peking UniversityXusheng Zhu(),Shanghai Jiao TongUniversityXiao Zheng(zheng_),XidianUniversityYajun Zhao(),ZTE CorporationYan Gao(

37、),Beijing Institute ofTechnologyYan Shi(),Xidian UniversityYang Liu(yangliu_),Dalian University ofTechnologyYang Zhang(y_),Southeast UniversityYanxia Cao(),China UnicomYanze Zhu(),Shanghai Jiao TongUniversityYasheng Jin(),Southeast UniversityYifan Li(),Nanjing University ofScience and TechnologyYife

38、i Yuan(),China MobileYiming Yu(),China MobileGroup Design Institute Co.,Ltd.Ying Gao(),Shanghai Jiao TongUniversityYiwei Sun(),China MobileYizhe Zhao(),University of ElectronicScience and Technology of ChinaYong Zeng(yong_),Southeast University,Purple Mountain LaboratoryYongli Ren(),TsinghuaUniversi

39、tyYongpeng Wu(),Shanghai JiaoTong UniversityYonina Eldar(yonina.eldarweizmann.ac.il),WeizmannInstitute of ScienceYu Han(),Southeast UniversityYuan Xu(yuan_),Zhejiang UniversityYuanbin Chen(chen_),Beijing Universityof Posts and TelecommunicationsYuanwei Liu(yuanweihku.hk),The University of HongKongYu

40、exia Zhang(),BeijingInformation Science and Technology UniversityYuhao Chen(yc3718princeton.edu),Princeton UniversityYuhua Jiang(),TsinghuaUniversityYuming Bai(),Beijing Institute ofTechnologyYunpu Zhang(yunpu.zhangmy.cityu.edu.hk),CityUniversity of Hong Kong/Southern University of Science andTechno

41、logyYunqi Sun(),ZTE CorporationYutong Zhang(),Peking UniversityYuxiao Wu(),Southeast UniversityZhaohui Yang(yang_),ZhejiangUniversityZhaolin Wang(zhaolin.wangqmul.ac.uk),Queen MaryUniversity of LondonZhaoyang Zhang(),Zhejiang UniversityZhe Wang(zhewang_),Beijing JiaotongUniversityzhen chen(),Univers

42、ity of MacauZhen Gao(),Beijing Institute ofTechnologyZheng Li(stones_),Zhengzhou UniversityZhengyu Zhu(),Zhengzhou UniversityZhenjun Dong(zhenjun_),SoutheastUniversityZhenqiao Cheng(),China TelecomZhenyu Zhang(),XiaomiZhiguo Ding(zhiguo.dingieee.org),Khalifa UniversityZhiqiang Yuan(),BeijingUniversi

43、ty of Posts and TelecommunicationsZhiwen Zhou(zhiwen_),SoutheastUniversityZhonglun Wang(),University ofElectronic Science and Technology of ChinaZhuo Xu(),Tsinghua UniversityChapter ContactsChapter 2:Xin Sun(),China MobileChapter 3:Yuan Xu(yuan_),ZhejiangUniversityChapter 4:Haiyang Miao(),BeijingUni

44、versity of Posts and TelecommunicationsChapter 5:Zhuo Xu(),TsinghuaUniversityChapter 6:Xidong Mu(x.muqub.ac.uk),QueensUniversity BelfastChapter 7:Shuang Zheng(),ZTECorporationCitation:Y.J.Zhao,L.L.Dai,J.H.Zhang,et al.“6GNear-field Technologies White Paper 2.0,”FuTURE Forum,Nanjing,China,Apr 2025.doi

45、:10.12142/FuTURE.202504001AcknowledgementThis white paper 2.0 has been written by an international expertgroup,led by FuTURE Forum,within a series of 6G whitepapers.White Paper 2.0:Writing Instructions and Key Updates1.Background and MotivationFollowing the release of the initial version last year R

46、1,we are now embarking on the development of White Paper2.0.This update is driven by the impending launch of the 6G study item by the Third Generation Partner Project(3GPP)in 2025.In anticipation of this significant milestone,it is imperative that we revise and enhance the existing white paperto ali

47、gn it with the evolving landscape of 6G technologies,particularly in the realm of near-field technology applications.The primary objectives of this update are to actively promote research and standardization efforts in near-fieldtechnologies,and to identify potential key scenarios and technologies f

48、or standardization,thereby providing crucialreferences for the 6G standardization process.2.Key Updates and Enhancements in White Paper 2.0Content Refinement and Update:White Paper 2.0 builds upon the foundation laid by its predecessor,refining andupdating the content to reflect the latest advanceme

49、nts and insights in the field.Existing sections have been reviewed andrevised to ensure accuracy,relevance,and comprehensiveness.Addition of Engineering Implementation and Standardization:Recognizing the growing importance of practicalimplementation and standardization in the 6G era,White Paper 2.0

50、places special emphasis on these aspects.A dedicatedchapter has been introduced to delve into the challenges and solutions of engineering implementation,as well as thestandardization processes and frameworks relevant to near-field technologies.R1 Y.J.Zhao,L.L.Dai,J.H.Zhang,et al.“6G Near-field Techn

51、ologies White Paper,”FuTURE Forum,Nanjing,China,Apr 2024.doi:10.12142/FuTURE.202404002.AbstractIn June 2025,Third Generation Partner Project(3GPP)is scheduled to officially launch its 6Gresearch project,marking the global evolution of 6G from a visionary stage toward a new phasecharacterized by crit

52、ical technological development,standardization,and practical applications.6Gnetworks leverage larger antenna apertures and higher frequency bands(e.g.,mid-bands,millimeterwaves,terahertz)to prioritize near-field characteristics.The integration of reconfigurable intelligentsurfaces(RIS),extremely lar

53、ge aperture arrays(ELAA),movable antennas(MA),and cell-freearchitectures will expand near-field dominance,creating a quasi-ubiquitous near-field propagationenvironment.From the perspective of spatial resource utilization,while conventional far-field systems can onlyseparate signals in the angular do

54、main,the transition toward near-field operation enhances the capacityto exploit the depth domain in future wireless systems.Near-field technology,equipped with numerousantennas,has attracted increasing attention in 6G networks for its potential to provide higher data ratesthrough beam focusing,preci

55、se angular-depth localization and sensing,as well as efficient wirelesspowertransfer.Near-fieldresearchrevealsaparadigmshiftinunderstandingpropagationcharacteristics of electromagnetic waves.These waves can no longer be simply regarded as planewaves but should be accurately modeled as spherical wave

56、s.This revised modeling emphasizes theimportance of previously overlooked electromagnetic phenomena in system modeling and design.These phenomena include spatial non-stationarity,finite-depth beam focusing,tri-polarization,andevanescent waves.Beyond conventional wave modeling,near-field research unl

57、ocks access to novelwavefronts,such as non-diffracting beams(including self-healing Bessel beams and curved Airybeams).Traditional communication algorithms,tailored for far-field conditions,may underperform in6G near-field environments,and model-agnostic algorithms might not fully capitalize on thes

58、e newcharacteristics.This white paper reviews typical application scenarios for near-field technologies in futurewireless networks.It then delves into the fundamental electromagnetic principles underlying near-fieldeffects and their disruptive impact on communication systems,systematically elucidati

59、ng theconstraints and enablers imposed on system architecture designwith a focus on the core metrics ofdegrees of freedom and capacity.Recognizing that channel characterization is foundational tocommunication system design,the paper provides a detailed discussion of near-field channel researchparadi

60、gms from both measurement and modeling perspectives,and further explores key technologiessuch as channel estimation,beamforming,and codebook design.It prospectively examines thecollaborative innovation between near-field technologies and other fields,including integrated sensingand communication,wir

61、eless power transfer,and physical layer security.At the engineering practicelevel,the latest advances in 6G spectrum allocation,the implementation of near-field propagationtechniques,and network deployment strategies are highlighted.Additionally,standardization impacts ofnear-field technologies for

62、6G are also provided.This white paper aims to establish a unified cognitiveframework for near-field technologies,bridging theoretical advances with standardization efforts andengineering implementation.Table of Contents1 Introduction.12 Near-field Application Scenarios.42.1 Near-field in Different F

63、requency Bands.42.2 Ultra Large Aperture Enabled Near-field.92.3 Integrated Sensing and Communication.132.4 Wireless Positioning.142.5 Simultaneous Wireless Information and Power Transfer.172.6 Physical Layer Security.182.7 Multiple Access.182.8 Massive IoT Communications.192.9 On-chip Wireless Comm

64、unications.193 Fundamental Theories of Near-field.223.1 Near-field Range Partitioning.223.2 Near-field Electromagnetic Physical Effects.283.3 Near-field Degree-of-freedom Theoretical Analysis.353.4 Near-field Performance Analysis and Measurement.414 Channel Measurement and Modeling of Near-field.554

65、.1 Near-field Channel Measurement.554.2 Near-field Channel Simulation.574.3 Near-field Channel Modeling.634.4 Bridging the Gap between Near and Far-Field Models.775 Transmission Technologies of Near-field.785.1 Near-Field Channel Estimation.785.2 Near-Field Beamforming.885.3 Near-Field Codebook Desi

66、gn.995.4 Near-Field Beam Training.1065.5 Near-Field Multiple Access.1155.6 Non-Coherent Communication Schemes.1185.7 Deployment of Near-Field Communication System.1206 Integration of Near-field Technology with Other Technologies.1236.1 Near-field Based Positioning.1236.2 Integrated Sensing and Commu

67、nication in Near Field.1426.3 Wireless Power Transfer in Near Field.1506.4 Physical Layer Security in Near-Field.1626.5 Near-Field Based OAM.1676.6 Near-Field Based Intelligent Communication.1706.7 Near-field On-chip Wireless Communications.1726.8 Near-field and Material Sensing.1767 Engineering Pra

68、ctice and Standardization.1787.1 Engineering Implementation.1787.2 Potential Standardization Impacts.1797.3 Technical Experiment and Prototype Test.1837.4 RIS-assisted Near-Field for Wireless Hot-Spot Applications.1918 Summarization and Prospects.193Reference.194List of Terms and Abbreviations.228Li

69、st of FigureFig.1.1Near-field Application Scenarios.2Fig.1.2Framework of Near-field Technology.3Fig.2.1IMT-2030 application scenarios and key capability indicators 10.4Fig.2.2Full spectrum of high,medium and low bands for the future 6G.5Fig.2.3Multi user near-field communication with beams pointing

70、to each user.6Fig.2.4Non-diffracting beams to circumvent obstacles.7Fig.2.5An illustration for near field communication feature.9Fig.2.6RIS assisted near-field application scenarios.10Fig.2.7Near field positioning model41.10Fig.2.8Different architectures of ELAA 42.12Fig.2.9MA for near-field communi

71、cations and sensing.13Fig.2.10Near field ISAC system40.14Fig.2.11High precision positioning based on near-field effects.15Fig.2.12RIS enabled near-field localization:(a).System Setup(b).Coordinate System.16Fig.2.13Schematic diagram of near-field wireless energy transmission.17Fig.2.14Left:Far field

72、secure communication using beam steering.Right:Near field securecommunication using beam focusing.18Fig.2.15Schematic diagram of near-field multiple access.19Fig.2.16Utilizing on-chip and inter-chip communication with antennas.20Fig.2.17Wireless interconnection between chips with different semicondu

73、ctor materials.20Fig.2.18Single-chip multi-core processors utilizing on-chip wireless communication technology.21Fig.3.1Far-field plane wavefront and near-field spherical wavefront and corresponding physicalspace normalized received energy.23Fig.3.2Near-field range for typical communication scenario

74、s.24Fig.3.3Illustration of the main factors leading to variant XPD over BS antennas.26Fig.3.4Comparison of non-uniform XPD distances with direction-dependent Rayleigh distances.27Fig.3.5Near-field electromagnetic radiation system diagram.29Fig.3.6Near-field multi-polarized spherical waves model.31Fi

75、g.3.7Near-field tri-polarized channel capacity.31Fig.3.8Near-field beam splitting effect schematic.32Fig.3.9Trend of beam gain with distance.33Fig.3.10Channel correlation versus antenna curve.33Fig.3.11Comparison of beamforming gain between UCA and ULA.34Fig.3.12Comparisons of far-field and near-fie

76、ld beamspaces.34Fig.3.13Fraunhofer boundary as a function of the frequency and antenna size 121.35Fig.3.14Technology-agnostic reference TX and RX antenna surfaces.35Fig.3.15Information-theoretical optimal communication system 121.36Fig.3.16DoF as a function of F=d2/ARDotted curves refer to simulatio

77、n 120.37Fig.3.17Electric field distribution corresponding to the 4 strongest communication modes.37Fig.3.18Extra DoFs in the reactive near-field.38Fig.3.19Nyquist sampling under isotropic scattering conditions.38Fig.3.20Singular Values of Near-Field SPD-MIMO.D denotes the transmission distance and N

78、 isthe number of transmit and receive antennas.40Fig.3.21EDoF versus array sparsity for sparse MIMO near-field communication.41Fig.3.22Near-field sum rate versus receive SNR.41Fig.3.23CDF of data rate for co-located and sparse array135.42Fig.3.24SNRs versus the number of array elements for different

79、 models136.43Fig.3.25Near-field beam focusing patterns under different array architectures138139.44Fig.3.26Achievable sum rates for modular and co-located arrays versus.44Fig.3.27XL-RIS aided communication system.45Fig.3.28Channel power gain versus the RIS-user distance.46Fig.3.29A MIMO transmitter(

80、a BS)communicates with a MIMO receiver(a UE)with the aid of anRIS 145.46Fig.3.30RIS-assisted Localization Performance.48Fig.3.31Fourier plane wave expansion channel model.49Fig.3.32Fourier plane wave expansion channel capacity simulation.49Fig.3.33Near-field electromagnetic channel capacity limit.50

81、Fig.3.34HMIMO Communication Application Scenarios.50Fig.3.35Three types of near-field holographic array topologies.51Fig.3.36Possible gain losses in near-field MIMO communications.52Fig.3.37Far-field and near-field capacities of planar arrays using different normalization methods.(a)Far-field analys

82、is.(b)Near-field analysis.53Fig.4.1Channel measurement platforms in time and frequency domains168182.56Fig.4.2(a)Near-field channel measurements with a virtual array based on VNA.(b)Channelimpulse response on the array elements183.57Fig.4.3XL-MIMO channel measurement 184.57Fig.4.4Distribution of AoD

83、 in the array domain at Rx1.57Fig.4.5Near field simulation in element level.58Fig.4.6The deployment of large antenna array(6GHz,1024elements).59Fig.4.7Absolute delay of a ray per element in BS antenna.59Fig.4.8AoA/AoD/ZoA/ZoD of a ray per element in BS antenna.59Fig.4.9Power gain of a ray per elemen

84、t in BS antenna.60Fig.4.10Phase of a ray per element in BS antenna.60Fig.4.11The position of antenna array and PEC sphere as well as the incident wave vector.61Fig.4.12The distribution of signal strength gain in antenna array due to H-pol and V-pol incident.61Fig.4.13The distribution of phase in ant

85、enna array due to H-pol and V-pol incident.61Fig.4.14The position of antenna array and random scatter as well as the incident wave vector.62Fig.4.15The distribution of signal strength gain in antenna array due to V-pol incident.62Fig.4.16The distribution of phase in antenna array due V-pol incident.

86、62Fig.4.17Spherical propagation with the SnS characteristic.63Fig.4.18(a)measurement result,(b)generation of channel model.64Fig.4.19Visibility region of the array and user.65Fig.4.20NUSW channel model for spatially discrete antennas.67Fig.4.21Greens function-based channel model for CAP antennas.68F

87、ig.4.22Modeling of near-field multi-polarized spherical waves.69Fig.4.23Multi-polarized channel capacity.69Fig.4.24XL-MIMO hybrid near and far field propagation environment.70Fig.4.25Illustration of the planar and spherical wavefronts in the RIS-enabled channel model.71Fig.4.26Illustration of the su

88、b-array partition model.71Fig.4.27Comparisons of the channel modeling accuracy based on the sub-array partition-basedalgorithm and that based on the far-field planar wavefront model under different motion time instantsand different RIS units.73Fig.4.28System model.74Fig.4.29Near-field angular domain

89、 channel,wave-number domain channel,and wave-numberdomain approximation results.77Fig.4.30Comparison of near and far-field models for different Tx-Rx distances 217.77Fig.5.1The energy spread effect in the angle-domain.78Fig.5.2Demonstration of DPSS codewords.79Fig.5.3Dictionary coherence comparison.

90、80Fig.5.4Space Partition Based on Joint Angular-Polar Domain Transform.81Fig.5.5MRDN-based channel estimation scheme.83Fig.5.6P-MRDN-based channel estimation scheme.83Fig.5.7RDN、CMAM and ASPP-RDN system models.83Fig.5.8Illustration of XL-RIS aided wireless system.84Fig.5.9Illustration of three-step

91、channel estimation.84Fig.5.10Illustration of the FRFT in near-field channel estimation.85Fig.5.11RIS architecture transmitting pilots using one RF-chain.87Fig.5.12Far-field beamforming and Near-field beamforming.88Fig.5.13Fully-Connected Delay-Phase Hybrid Beamforming.89Fig.5.14Partially-Connected D

92、elay-Phase Hybrid Beamforming.89Fig.5.15Serially-Connected Delay-Phase Hybrid Beamforming.89Fig.5.16The near-field beam patterns in different scenarios.91Fig.5.17The distributions of Fresnel zones.92Fig.5.18The location-driven beamforming frame structure.92Fig.5.19Sum-rate versus BS transmit power.9

93、3Fig.5.20Energy efficiency versus BS transmit power.93Fig.5.21Extremely large-scale MIMO system with CPU and LPU collaborative processing.94Fig.5.22PAA-RIS dual beamforming scheme.95Fig.5.23Rate heatmap comparison.95Fig.5.24Comparison of uniform and non-uniform sparse arrays in terms of the beam pat

94、tern.96Fig.5.25Comparisons of different arrays in terms of sum-rate for different numbers of users.96Fig.5.26Bessel beam intensity distribution and the impact of quantization.98Fig.5.27Illustration of FRFT codeword quantization performance.100Fig.5.28Angle-displaced near-field codebook design method

95、.100Fig.5.29Principle and phase distribution of far-and near-field codebooks.100Fig.5.30Principle of the proposed flexible beamwidth control scheme.101Fig.5.31Codeword coverage discretization in the near-far field codebook.102Fig.5.32Illustration of XL-MIMO system with single polarization ULA.102Fig

96、.5.33Performance gain of near-field codebook over far-field codebook.103Fig.5.34Distribution of near-field indicator.104Fig.5.35Trade-off between performance and overhead.104Fig.5.36Performance gain of near-field codebook over far-field codebook with near fieldidentification.105Fig.5.37Neural networ

97、k structure for near-field beam training.109Fig.5.38Sum-rate versus SNR for multi-beam scheme.110Fig.5.39Sum-rate versus SNR for DFT codebook scheme.110Fig.5.40Average VR ratio versus user distance.111Fig.5.41Capacity versus user distance.112Fig.5.42XL-MIMO-enabled HSR communication systems.112Fig.5

98、.43The proposed MNBT scheme.113Fig.5.44Performance comparison between the MNBT-based scheme and traditional beam trainingscheme.113Fig.5.45UE distribution.114Fig.5.46L1-RSRPdifferencebetweennear-fieldandfar-fieldL1-RSRP(UEdistributionOption1).114Fig.5.47L1-RSRPdifferencebetweennear-fieldandfar-field

99、L1-RSRP(UEdistributionOption2).115Fig.5.48Comparison between far-field SDMA and near-field LDMA.116Fig.5.49Multiple-access in near-field for users with same angular orientation 302.116Fig.5.50Illustration of near-field NOMA designs.117Fig.5.51Deployment scenarios of XL-arrays(taking RIS as an exampl

100、e).121Fig.5.52A hybrid communication architecture based on near-field relays.122Fig.6.1Near-field signal model and far-field signal model.123Fig.6.2Near-field positioning and attitude sensing.124Fig.6.3User positioning with sub-arrays.126Fig.6.4The received phase of a spherical wave on a large array

101、 and its approximation with planarwaves on 5 subarrays.126Fig.6.5RIS-aided localization:BS 2 and BS 4 can be seen by the user through the RIS thus ensuringat least 3 points of view as needed for 2D localization.127Fig.6.6RIS-assisted localization in a near-field NLOS scenario.28 GHz system with 250

102、MHzbandwidth.128Fig.6.7Schematic diagram of two-dimensional DoA estimation based on RIS and non-uniform timemodulation 335.128Fig.6.8Near-field target localization based on CNN 340.129Fig.6.9RIS-assisted THz multi-user positioning systems.130Fig.6.10RMSE versus the number of RIS elements.130Fig.6.11

103、Localization system coordinate.131Fig.6.12PEB as a function of distance to the RIS using random,directional and positional RIS phaseprofiles.132Fig.6.13Data Rate versus RIS distance.132Fig.6.14Schematic diagram of near-field beam squint.133Fig.6.15Schematic diagram of near-field controllable beam sq

104、uint.133Fig.6.16Localization in NLOS by exploiting a metaprism(each color represents a differentsubcarrier).134Fig.6.17A single BS localization system with clock asynchronism and without LoS path.135Fig.6.18A single BS near-field sensing system with the division of sub-array.137Fig.6.19A near-field

105、XL-MIMO system for target localization.138Fig.6.20Comparison of localization accuracy of different near-field localization algorithms.138Fig.6.21Angle spectrum.139Fig.6.22Range spectrum.140Fig.6.23The recording and reconstructing procedure of back projection method.140Fig.6.24Typical reconstruction

106、results of back projection method.141Fig.6.25The reconstruction results of(a)ULA and(b)sectored UCA with two targets in the nearfield.141Fig.6.26Near-field radar sensing with XL-MIMO.142Fig.6.27CRB of angle for monostatic sensing.144Fig.6.28CRB of range for bistatic sensing.144Fig.6.29Far-field Velo

107、city Sensing.145Fig.6.30Near-field Velocity Sensing.145Fig.6.31Communication-assistednear-fieldsensingandsensing-assistednear-fieldcommunication.147Fig.6.32Vision-aided positioning and location-based near-field beam focusing.148Fig.6.33Mixed-field networked sensing with widely deployed BSs.149Fig.6.

108、34Experimental environment for near-field sensing,and measurement results for sensingaccuracy in terms of signal bandwidth 370.150Fig.6.35Adaptiveintelligentnear-fieldchargingsystembasedonprogrammablemetasurface377.152Fig.6.36Schematic diagram of multi-target WPT system based on quasi-Bessel beams 3

109、79.152Fig.6.37Schematic diagram of adaptive wireless-powered network 340.153Fig.6.38Block diagram of wireless energy harvesting system.154Fig.6.39Schematic diagram of the rectifying metasurface.154Fig.6.40SWIPT systems based on(a)frequency diversity and(b)polarization diversity.155Fig.6.41Representa

110、tion of schematic with the fabricated view of the developed PA.156Fig.6.42Simulated IV curve of 10-W PA at 28 dBm input power.157Fig.6.43Obtained results of(a)PAE,Pout,DE,and Gain(b)IMD3 and IMD5 w.r.t Pout of PA.157Fig.6.44Near-Field SWIPT.158Fig.6.45Continuous-aperture RHS-based WPT performance 40

111、5.159Fig.6.46Continuous-aperture RHS-based SWIPT transceiver 405.160Fig.6.47(a)Thefrontviewofdiscrete-apertureRHS；(b)Therightviewofdiscrete-apertureRHS.161Fig.6.48Discrete-aperture RHS-based SWIPT transmitter.161Fig.6.49The trade-off between the WIT and WPT performance of the discrete-aperture RHS-b

112、asedtransceiver.162Fig.6.50Near-Field PLS.163Fig.6.51Near-field secure wireless communication with DAM.164Fig.6.52The absolute square of the normalized inner product for two near-field channel vectors,where11(,)(30 m,0)rand221(,)(45 m,)r.165Fig.6.53Secrecy rate versus the number of antenna elements

113、at Alice.165Fig.6.54Beam diffraction in near-field.166Fig.6.55A near-field PLS system.167Fig.6.56Comparison of the electric field between(a)conventional OAM beam and(b)non-diffraction Bessel vortex beam 427.169Fig.6.57Schematic diagram of(a)full aperture sampling and receiving method and(b)partialap

114、erture sampling and receiving method 429.170Fig.6.58An illustration of the near-field based semantic communication system.170Fig.6.59An illustration of the near-field based federated learning framework.171Fig.6.60Near-field intelligent beamforming(left)and performance comparison(right).172Fig.6.61Co

115、mmonly used on-chip communication system block diagram432.172Fig.6.62Layout representation for the intra-chip communication arrangement433.173Fig.6.63Cross-section of the on-chip top-hat antenna 437.173Fig.6.64On-chip antenna based on GaN technology 438.174Fig.6.65(a)Typical coil array structure,(b)

116、Coil array structure with a shielding pattern,(c)Proposedzigzag-shaped coil array for wireless chip-to-chip communication.175Fig.6.66Illustration of wireless in-plane/out-of-plane intra-/interchip communications utilizingTGV-integrated antennas in 3D System-in-Packaging(SiP).176Fig.6.67Sensing scena

117、rio.177Fig.7.16G Potential Spectrum.179Fig.7.2Activities to promote the standardization of the near-field technology.183Fig.7.3The diagram of the measuring system.184Fig.7.4Overview of the LoS measurement.186Fig.7.5Overview of the measurement.187Fig.7.6Properties of cluster parameters.188Fig.7.7Powe

118、r-delay-angle profile of channels for(a)Tx2-Rx2,(b)Tx3-Rx3.189Fig.7.8Channel capacity at various BS-UE distances.191Fig.7.9Indoor channel sounding measurement campaign with a 3.5 GHz RIS.191Fig.7.10RIS near-field beam pattern for the reflection angle =40,=0 at 2 m distance(r),andthe corresponding pe

119、ak of the PDP in the near-field for both RIS on and RIS off cases.192Fig.7.11RIS linear phase gradient far-field response pattern for the reflection angle =40,=0,and the corresponding peak of the PDP in the far-field at 12 m separation distance between RIS andRX for both RIS on and RIS off cases.192

120、Fig.7.12Raytracing simulations in the near field with and without RIS.1921 IntroductionAs the commercialization of 5G wireless networks gains momentum,there is a growing emphasison exploratory research into the upcoming 6G wireless networks.This era of technologicaladvancement witnesses 6G networks

121、being characterized by a more visionary and performance-drivenethos compared to their predecessors.In June 2025,3GPP will officially launch its 6G research project,marking the global evolution of 6G from a visionary stage toward a new phase characterized by criticaltechnological development,standard

122、ization,and practical applications.Traditional wireless networks,spanning from 1G to 5G,predominantly operate within a spectrumbelow 6 GHz,often below 3 GHz.Due to the physical constraints on antenna arrays and theproportional relationship between element spacing and wavelength,these spectrum bands

123、necessitatethe use of relatively small numbers of antennas.Consequently,the combined effect of theselower-dimensional antenna arrays and lower frequency bands confines the range of wireless near-fieldcommunication(NFC)to mere meters or even centimeters,thereby shaping the design of these systemsarou

124、nd far-field assumptions.However,the transition to 6G networks is marked by the adoption of larger antenna apertures andhigher frequency bands,such as new mid-frequency,millimeter-wave(mmWave),and terahertz bands,which accentuate near-field characteristics12,as shown in Table 1.1.The integration of

125、emergingtechnologies such as Reconfigurable Intelligent Surface(RIS)134,extremely large aperture arrays(ELAA)5,movable antenna(MA)6,and cell-free networks7is expected to amplify the prevalenceof the near-field scenario in future wireless networks.Consequently,this shift challenges traditionalfar-fie

126、ld plane wave assumptions and underscores the need to rethink strategies for spatial resourceutilization8.While conventional systems have effectively exploited far-field spatial resources,theexploration and utilization of near-field spatial resources in 6G networks promise to introduce novelphysical

127、 dimensions to wireless communication systems 9.This shift towards near-field regions in6G networks catalyzes a new wave of research in near-field technology paradigms.Table 1.1 Near field range of typical scenarios(Rayleigh distance)Df2.6 GHz(low band)7 GHz(Mid band)28 GHz(mmWave band)220 GHz(THz b

128、and)0.5m4 m12 m483721.6m60 m119 m476/3.0m210 m420 m/In the realm of near-field technology,the nuanced propagation behaviors of electromagnetic waves necessitate a departure from plane wave approximations to embrace spherical wave treatments.This paradigm shift brings forth a plethora of hitherto ove

129、rlooked electromagnetic phenomena,including spatial non-stationarity,tri-polarization,evanescent waves,and the capacity for near-field focusing,all of which pose challenges to the efficacy of traditional communication algorithms within the context of 6Gs near-field environment.1/2322/232Harnessing n

130、ear-field effects holds the promise of facilitating the realization of a broader spectrumof application scenarios and key performance indicators outlined in IMT-2030.This white paperexplores potential application scenarios based on near-field technology,as shown in Fig.1.1.Fig.1.1 Near-fieldApplicat

131、ion ScenariosThrough meticulous examination of both theoretical foundations and technological advancements,we have crafted a preliminary framework for near-field technology,as illustrated in Fig.1.2.Ourdiscourse commences with an exploration of the definition of near-field in electromagnetic theory,

132、tracing the origins of near-field electromagnetic effects and their ramifications on existingcommunication systems.Drawing upon an extensive body of literature,this article provides anencompassing synthesis of near-field effects on communication system design and performance,withparticular emphasis

133、on degrees of freedom and communication capacity.A nuanced comprehension of near-field channel characteristics and models is pivotal forcommunication system design and evaluation.Thus,our article underscores the imperative forexhaustive channel measurements and precise channel characterization.Furth

134、ermore,we delve intonear-field transmission technologies,encompassing facets such as channel estimation,beamforming,codebook design,beam training,multiple access technology,system architecture,deploymentconsiderations,and implications for standardization.Then,we explore the convergence of near-field

135、technology with other domains,including positioning,wireless power transfer(WPT),physical layersecurity,orbital angular momentum-based near-field,AI-driven communications,and near-fieldon-chip wireless communications.Additionally,we highlight progress in both engineering andstandardization,covering

136、the primary 6G spectrum allocation,enabling technologies for near-fieldpropagation,and network deployment strategies.3/232Fig.1.2 Framework of Near-field TechnologyDespite notable advancements in the research on near-field propagation characteristics,a dearth ofliterature offers a systematic synthes

137、is of near-field technology.Therefore,this article endeavors tobridge this gap by furnishing an all-encompassing summary of near-field technologys applicationscenarios,fundamental theories,channel measurement and modeling methodologies,transmissiontechnologies,and integration with allied fields.This

138、 white paper aims to establish a unified cognitiveframework for near-field technologies,bridging theoretical advances with standardization efforts andengineering implementation.4/2322 Near-field Application ScenariosIn November 2023,the International Telecommunication Unions 5D Working Group on Wire

139、lessCommunications(ITU-R WP5D)released a framework and overall goal proposal for the developmentof IMT towards 2030 and beyond,proposing typical 6G scenarios and capability indicator systems,asshown in Fig.2.1 1011.6G scenarios include immersive communication,ultra-large-scaleconnections,extremely h

140、igh reliability and low latency,artificial intelligence and communication,integration of perception and communication,ubiquitous connections,etc.The key 6G capabilityindicators include 9 enhanced 5G capabilities and 6 new capability dimensions,including peak datarate,user experienced data rate,spect

141、rum efficiency,regional traffic,connection density,mobility,latency,reliability,security privacy elasticity,coverage,sensing related indicators,AI-applicableindicators,sustainability and positioning 12.6G is expected to continue the efforts of 5G-Advancedin terms of continuously enhancing connection

142、 experiences for mobile users and enabling more verticalindustries 13.Fig.2.1 IMT-2030 application scenarios and key capability indicators 10.To meet the spectral efficiency requirements of IMT-2030,it is necessary to further explore theapplication potential of higher frequency bands and larger-scal

143、e arrays.Concurrently,the ultralarge-scale-arrays in higher frequency bands will bring near-field effects.The so-called near-fieldeffectrefersto thesituationwhere,undercertaindistanceconditions,theassumptionofelectromagnetic waves as plane wavefront in the far-field no longer holds and needs to be m

144、odeled asspherical wavefront.The spherical wavefront carries not only angle information,but also depthinformation.Electromagnetic beam focusing occurs concurrently in both the angular and depthdomains,forming near-field beam focusing 14.By utilizing the near-field effect,more applicationscenarios an

145、d key performance indicators of IMT-2030 can be better achieved,such as integratedsensing and communication(ISAC),positioning,security,mobility,etc.This section will explain theapplication scenarios of near field based on the above analysis.2.1 Near-field in Different Frequency BandsThe expansion of

146、 bandwidth and the increase of antennas will bring greater capacity and higherspectral efficiency to wireless communication systems.Typical 2G,3G,4G,and 5G communicationsystems use bandwidths of 0.2 MHz,5 MHz,20 MHz,and 100 MHz,respectively,and largerbandwidths will be required for 6G in the future.

147、5/232In May 2023,Chinas Ministry of Industry and Information Technology(MIIT)issued a newversion of the Regulations of the Peoples Republic of China on the Division of Radio Frequencies(MIIT Decree No.62),which is the first in the world to use all or part of the bandwidth of the6425-7125 MHz frequen

148、cy band,totaling 700 MHz,for the 5G-A/6G system 15.In December of thesame year,the International Telecommunication Union(ITU)held the World RadiocommunicationConference 2023(WRC-23)in Dubai,UAE,and completed a new round of revisions to the RadioRules,newly dividing 6G spectrum resources in the mid-b

149、and of 6425-7125 MHz with a total of 700MHz of bandwidth for most of the countries in the world 16.In December 2023,the internationalstandardization organization,3GPP,held its Edinburgh,UK meeting,in which the first projects forRel-19,the second version of the 5G-Advanced standard,including eight ar

150、eas such as channelmodeling studies for the new 7-24 GHz spectrum are established 17.As reported in study of 6G midband frequency by Nokia,a new spectrum ranges:4.4004.800 GHz,7.1258.400 GHz and14.80015.350 GHz are potentially available for 6G,subject to further study in the WRC-27 cycle 18.The repo

151、rt also highlights about a US spectrum pipeline outside the WRC process concerning the lower3 GHz band(3.1003.450 GHz),which is being considered for shared use with military radar,and the12.7 GHz band(12.70013.250 GHz),which will be exclusively used for licensed mobile broadband.Compared to the sub-

152、6 GHz low-frequency band,which is widely used in 5G,and thehigh-frequency bands,such as millimeter wave and terahertz,which may be used in 6G in the future,the mid-frequency band,which combines the advantages of both coverage and capacity,is of greatvalue for the wide-area high-capacity coverage of

153、6G,and it is expected to be one of the fundamentalfrequency bands for 6G,as shown in Fig.2.2.Fig.2.2 Full spectrum of high,medium and low bands for the future 6GIn the future,6G high,medium,and low frequency bands are likely to face near-fieldcommunication.6G Technologies,a research report published

154、 by the 6G Alliance in June 2022,alsoclearly points out the necessity of researching near-field in 6G high,medium,and low frequency bands18.2.1.1 High Frequency Band TransmissionmmWave and terahertz(THz)wireless communication can utilize large available bandwidth toimprove data transmission rates,ma

155、king it one of the key technologies for the next generation ofcommunication systems 192021.In order to compensate for the path loss of high-frequencytransmission,base stations(BS)operating on these frequency bands will be equipped with large-scaleantenna arrays.The application of large-scale antenna

156、 arrays will increase the possibility that the usersin high-frequency communication falling into the near-field region,while traditional wireless systemstypically operating in the far-field range.Under millimeter wave and terahertz conditions,the near-field6/232distance of relatively small antennas/

157、surfaces can also reach several tens of meters.For example,thenear-field distance of a uniform linear array with 128 antennas working at 300 GHz would be 65meters,which covers a relatively large area.Namely,the far-field plane wave assumption onelectromagnetic fields is no longer applicable at actua

158、l communications distances.Thus,a near-fieldmodel of spherical waves should be used.The management of spherical wavefront can be transformedinto flexible beamforming ability.For example,utilizing the spherical wavefront can focus theelectromagnetic waves into a spot rather than traditional beam stee

159、ring under far-field condition,whichis referred as the concept of beam focusing in recent literature22.Beam focusing can support multipleorthogonal links even at similar angles.The ability to focus beams in large-scale multiple input multiple output(MIMO)systems largelydepends on the signal processi

160、ng capability of the antenna array,with different processing capabilitiesin different architectures.The most flexible solution for a given radiation element array is full-digitalarchitecture,where each antenna element is connected to a dedicated radio frequency(RF)chain.Under this architecture,the t

161、ransceiver can simultaneously control infinite beams in multiple directions,greatly improving spatial flexibility.However,when deploying large-scale arrays in 5G and moreadvancedcommunicationsystems,implementingfull-digitalarchitecturesbecomesextremelychallenging due to increased costs and power con

162、sumption.To alleviate this,large-scale MIMOcommunications adopt a hybrid analog-digital architecture.This hybrid architecture combines lowdimensional digital processing and high-dimensional analog precoding,typically achieved throughphase shifter interconnections,resulting in fewer RF links than ant

163、enna components.Another emergingtechnology for effectively implementing large-scale arrays is dynamic meta-surface antennas,whichcan programmatically control the transmit/receive beam patterns,provide advanced analog signalprocessing capabilities,and naturally achieve frequency chain reduction witho

164、ut the use of dedicatedanalog circuits.This also densifies antenna components,thereby improving focusing performance.Reference23 explores multi-user communications in near-field utilizing various antenna architectures,including all digital arrays,hybrid architectures based on phase shifters,and dyna

165、mic meta-surfaceantennas,as well as the impact on downlink multi-user systems when forming focused beams.Fig.2.3 Multi user near-field communication with beams pointing to each user(a)towards each user in three-dimensional space;(b)beam steering in far-field,leads to interferencebetween users at the

166、 same angle;(c)beam focusing in near-field,with minimal interference23In addition,new types of wavefronts become available beyond spherical waves when operating inthe near field24.By wavefront,we refer to the imaginary surface representing all points in a wavethat are in the same phase at a given ti

167、me.Among others,the use of Bessel beams has been recently7/232proposed2526.Bessel beams are no-diverging beams that focus the signal along a line.To generatean ideal Bessel beam,one would need an infinite aperture(i.e.,an infinite lens or antenna array orreflect-array).When using a finite aperture,t

168、he Bessel beam only exist until a maximum distancedelimited in the near-field.Focusing the power along a line,instead than on a point,can drasticallyreduce the amount of channel state information(CSI)to ensure the reliable transmission of information.Moreover,Bessel beams are self-healing,i.e.,even

169、when they are partially blocked,the signal isregenerated to the original level after the obstacle 2728.This can be leveraged to overcomeblockage,which is one of the main problems for high-frequency systems.Another type ofnon-conventional wavefronts that can be generated in the near field are Airy be

170、ams 29.These are alsonon-diffracting beams that,in this case,focus along a curving line.This allows for example tocircumvent obstacles(See Fig.2.4).Fig.2.4 Non-diffracting beams to circumvent obstaclesa)Illustration of a Bessel beam,focusing a long a line in the near field;b)Phase distribution of aB

171、essel beam;c)Bessel beam regeneration after an obstacle;d)Phase distribution of an Airy beam;e)Acurving Airy beam.Traditionally,Bessel beams and Airy beams have been generated at optical frequencies andutilizing different types of lenses.For example,axicons are utilized to generate Bessel beams.Neve

172、rtheless,these wavefronts can be generated utilizing arrays,reflect-arrays and meta-surfaces30with a large number of elements3132 at least phase control per element.Operating with new typesof wavefronts drastically changes many well-known concepts 33,including the study of interferenceacross differe

173、nt type of beams,CSI estimation,joint ultrabroadband waveform and wavefront design31,and even physical layer security34.8/2322.1.2 Mid Frequency Band TransmissionIn September 2020,3GPP completed the approval of the 6 GHz licensed frequency band,initiatedthe standardization of mid frequency band RF.T

174、hen,3GPP successfully completed the standardizationof the 6425-7125 MHz licensed frequency band in September 2022.Subsequently in December 2023,3GPP further listed channel modeling in the 7-24 GHz mid frequency band as one of the earlieststandardtopics inthesecondstandardversionof5G-Advance,continui

175、ngpromotingthestandardization process of the mid frequency band.At the same time,in May 2023,the Ministry ofIndustry and Information Technology of China issued a new version of the Regulations on theClassification of Radio Frequency in the Peoples Republic of China,which clearly divides the 6 GHzmid

176、 frequency band for 5G/6G mobile communication systems.The 6 GHz frequency band has acontinuous large bandwidth of 1200 MHz,which has lower propagation path loss and strongercoverage ability compared to the high-frequency band.It has both coverage and capacity advantagesand can be used for wide area

177、 high-capacity coverage in 6G communication.Therefore,the midfrequency spectrum resources will become one of the important alternative frequency bands for 6Gcommunication.Compared to the sub-6 GHz frequency band of 5G communication,the increase in communicationfrequency leads to weaker coverage due

178、to large propagation losses in materials and smaller antennasin mid frequency centimeter wave communication.In order to compensate for the high path loss duringthe propagation of intermediate frequency signals,the antenna size of intermediate frequency BSsneeds to be further increased.With the incre

179、ase of communication frequency band and BS antennaarray aperture,the near-field range of intermediate frequency communication will also be significantlyexpanded.Taking the 7 GHz communication system and BS with 1.6-meter antenna array as anexample,its near-field range exceeds 100 meters.In the forma

180、l project of the 3GPP intermediatefrequencychannelmodelingstandard,near-fieldcharacteristicsandspatialnon-stationarycharacteristics are considered as new characteristics of mid frequency channels,becoming importantconsiderations for improving the 3GPP channel model 17.Therefore,near-field spherical

181、wavecommunication will become an important scenario for mid frequency transmission.Due to the shorter wavelength of mid frequency centimeter waves and the further increase inantenna size,mid frequency system is more likely to form high-resolution narrow spatial beams,therefore achieving higher spati

182、al degrees of freedom.Typical application scenarios for this bandinclude single user multi-stream or higher-order multi-user multiplexing scenarios.The modeling andmeasurement of near-field spherical wave channels in the mid frequency range will provide a channelmodel foundation for mid frequency co

183、mmunication.The near-field spherical wave propagation modelis expected to provide additional single user transmission spatial freedom.The focusing characteristicsof near-field spherical waves can also be used for high-order multi-user multiplexing methods,furtherimproving the throughput performance

184、of 6G new mid frequency communication systems.9/232Fig.2.5 An illustration for near field communication featureFor users located in the near field region,spherical wavefronts are utilized for communicationcompared to plane wavefronts in the far field region.352.1.3 Low Frequency Band TransmissionThe

185、 low frequency band(FR1,Sub-6GHz)defines the baseline coverage range of cellularnetworks.While expanding to higher frequency bands,6G will also fully utilize the advantages of FR1frequency band for wide coverage and deep penetration to improve spectrum efficiency and breakthrough bandwidth bottlenec

186、ks.Large scale MIMO can be used in the low frequency range to improvethe spectral and energy efficiency of 6G systems while ensuring wide coverage.If traditional large-scale MIMO is deployed in the low frequency range,it will face limitations onantenna size due to tower or BS deployment.Modular or d

187、istributed large-scale MIMO,as well asmeta-surface antennas,are expected to overcome size limitations and reduce the requirement of halfwavelength distance between antenna units through compact antenna arrays.On the other hand,traditional cell-based deployment strategies pose challenges such as feas

188、ibility,processing,andarchitectural complexity.Therefore,large-scale MIMO in the low frequency range may adopt multipanel,multi transceiver nodes,non-cellular,and irregular large-scale distributed network deployment.In this scenario,further research is needed on distributed deployment strategies,the

189、 potential demandfor new channel models from non-uniform antenna panels,large antenna arrays,and near-field effectswhen users may approach access points.Also,research that focuses on exploring efficient referencesignal designs,channel acquisition frameworks for far-field and near-field channels,furt

190、her evaluatingthe potential of artificial intelligence in channel acquisition would be valuable.2.2 Ultra Large Aperture Enabled Near-field2.2.1 RIS Enabled Near-fieldRIS is considered as one of the key potential technologies in 6G,consisting of a large number oflow-cost reconfigurable units 36.Depl

191、oying RIS in wireless networks can effectively adjust thewireless channel between transmitters and receivers,thereby improving communication quality andcoverage range,as already demonstrated by field trials 37.One of the typical applications of RIStechnology is to obtain sufficient beamforming gain

192、through hundreds or even thousands ofcomponents for coverage blinding in millimeter wave and terahertz communications.The larger RISarray and higher operating frequency further expand the near-field area of RIS assisted communicationlinks 38.RIS is typically used to establish a direct connection cha

193、nnel between transmitters/receivers.In the far-field region,the rank of the channel is usually small,which restricts the spatial multiplexinggain of the channel.On the other hand,due to the nonlinear changes in signal amplitude and phasecaused by spherical waves,near-field channels have better rank

194、conditions,which can effectively10/232improve the multiplexing gain and spatial freedom of the system 39.When users are located in theradiation near-field region,even if multiple users are at the same radiation angle,different near-fieldcodebooks can be configured on the intelligent meta-surface to

195、reduce co-channel interference throughbeam focusing,supporting multiple coexisting orthogonal links to achieve space division multipleaccess 40,as shown in Fig.2.6.Similarly,the degrees of freedom provided by the sphericalwavefront and the near-field radiation wave carrying both angle and distance i

196、nformation furtherenhances the accuracy of wireless positioning services and perception,as shown in Fig.2.7.On theother hand,this also means that the spatial non-stationarity of the channel is intensified,which willbring challenges to channel estimation,codebook design,beam training complexity,mobil

197、itymanagement,signaling design,and other aspects.Fig.2.6 RIS assisted near-field application scenarios.Fig.2.7 Near field positioning model412.2.2 ELAA Enabled Near-fieldELAA is essential to the candidate technologies for 6G such as Extremely Large-Scale MIMO(XL-MIMO).Compared to 5G massive MIMO,ELA

198、A for 6G not only means a sharp increase in thenumber of antennas but also results in a fundamental change of the electromagnetic(EM)characteristics.With the significant increase of the antenna number and carrier frequency in future 6Gsystems,the near-field region of ELAA will expand by orders of ma

199、gnitude.The two commonly usedELAA architectures are co-located and distributed ELAAs,as shown in Fig.2.8(a)and(b).Theantenna elements of co-located ELAA are typically separated by half wavelength,and its physicaldimension is limited by the continuous platform 42.By contrast,distributed ELAA is an ar

200、chitecturethat antennas are widely distributed over a vast geographical region with multiple separated sites,11/232which are interconnected by the backhaul/fronthaul links,so as to perform joint signal processing.However,distributed ELAA,e.g.,cell-free ELAA,usually requires the sophisticated site co

201、ordinationand high backhaul/fronthaul capacity.In order to complement for existing ELAA architectures,the works 4344 propose a novelmodular ELAA architecture.As illustrated in Fig.2.8(c),the antenna elements of modular ELAA areregularly mounted on a shared platform in a modular manner.Each module is

202、 comprised of amoderate/flexible number of array antennas with the inter-element distance typically in the order of thesignal wavelength,while different modules are separated by the relatively large inter-module distance,so as to enable conformal capability with the deployment structure in practice.

203、For example,themodular ELAA with interlaced modules can be embedded into the discontinuous wall spaced bywindows,like facade circumstances of shopping malls,factories or office buildings.Compared toco-located ELAA with the same number of antenna elements,modular ELAA not only has thecharacteristic o

204、f flexible deployment,but also a higher spatial resolution due to the larger physicaldimension.However,since the inter-module distance is much larger than half wavelength,modularELAA will lead to the undesired grating lobes.On the other hand,different from the distributed ELAAarchitecture,modular EL

205、AA typically performs joint signal processing,without having to exchange orcoordinate sophisticated inter-site information,which may ease the requirement of synchronization andreduce hardware cost associated with the backhaul/fronthaul links for distributed ELAA.Uniform sparse ELAA is array architec

206、ture where the inter-element spacing is larger thanhalf-wavelength,as illustrated in Fig.2.8(d),which is a special case of modular ELAA.In general,uniform sparse ELAA results in a narrower main lobe due to the higher spatial resolution,which canprovide a significant interference suppression gain in

207、scenarios with densely located users 45.Similarto modular ELAA,uniform sparse ELAA gives rise to the undesired grating lobes,due to theinter-element spacing much larger than half wavelength.It is worth mentioning that the above four array architectures are suitable for different applicationscenarios

208、.For example,co-located,modular and uniform sparse ELAAs can all be used to supportcellular hotspot communications,while modular and uniform sparse ELAAs achieve a highertransmission rate in scenarios with densely located users.Besides,distributed ELAA is able to providea better communication servic

209、e for geographically widely distributed users.Thus,the above fourarchitectures complement each other,and the choice of appropriate ELAA architecture depends on theactual application scenario.(a)co-located ELAA(b)Distributed ELAA12/232(c)Modular ELAA(d)Uniform sparse ELAAFig.2.8 Different architectur

210、es of ELAA 422.2.3 Cell-Free Enabled Near-fieldUnlike the classic cellular communication architecture,the Cell-Free communication architectureachieves a user centered communication paradigm by deploying a large number of access nodes in adistributedmanner,effectivelyovercomingintercellinterference,a

211、voidingcommunicationinterruptions,and further improving the performance of next-generation 6G mobile communication.Based on the Cell-Free communication architecture,the equivalent array aperture is significantlyexpanded due to the distributed deployment of multiple arrays,and the near-field spherica

212、l wave effectis more significant.Meanwhile,due to the denser distribution of access nodes and shortercommunication distances,users will have a higher probability of being in the near-field range.Inaddition,due to the collaborative nature of non-cellular communication architectures,users may beserved

213、 simultaneously by multiple access nodes with different antenna sizes and distances,which maybe located in the far-field or near-field range of different nodes,facing more complex mixed far-fieldand near-field communication scenarios.Therefore,cellular free near-field communication will be oneof the

214、 important application scenarios for future 6G.The modeling of near-field spherical wave channels can provide a model foundation for cellularfree communication systems.Due to its significant near-field spherical wave effect,considering thenear-field spherical wave property can further improve the op

215、timization accuracy of access nodes incellular free architectures.At the same time,beamforming methods that are compatible with near-fieldspherical waves and far-field plane waves,efficient far-field cellular free communication channelestimation,and beam training schemes can better adapt to near-fie

216、ld communication scenarios,furtherimproving the performance of cellular free communication systems.2.2.4 MA Enabled Near-Field Communication and SensingMovable antenna(MA)technology has recently been introduced in wireless communicationsystems to control the movement of antennas at the Tx/Rx for imp

217、roving wireless channel conditionsand communication performance 6.There are various practical methods that can be used to enableantenna movement,such as mechanical motors,microelectromechanical system(MEMS),liquid/fluidantennas,deployable structures,etc 46.Due to their flexible movement capability,M

218、As can fullyexploit the wireless channel spatial variation.For example,they can significantly enhance the spatialdiversity performance,in terms of receiver signal power improvement and interference mitigation,ascompared to conventional fixed antennas 47-49.Besides,for multi-MA aided MIMO and/ormulti

219、user communication systems,the channel matrices can be reshaped by antenna positionoptimization to increase the spatial multiplexing gain and thus the wireless channel capacity 5051.13/232In addition,the exploiting of 3D position and 3D rotation can achieve the highest degrees of freedom(DoFs)in ant

220、enna movement for enhancing wireless communication performance,which renders thedesign of six-dimensional movable antenna(6DMA)systems 52-54.Moreover,by integratingmultiple MAs into an array,more flexible beamforming can be realized by jointly designing the arraygeometry and beamforming vector 5556.

221、Since the effective array aperture scales with the size ofthe antenna moving region,enlarging the antenna moving region expands the near-field region of theTx/Rx for communication as well as sensing 57-59,as shown in Fig.2.9.Fig.2.9 MA for near-field communications and sensingDifferent from the ELAA

222、 which requires an extremely large number of antenna elements and RFfrontends,the number of MAs is moderate and can be kept constant even with the increasing movingregion size.Thus,MAs can help reduce the hardware cost and RF power consumption as compared toELAA.The performance advantages of MA syst

223、ems,such as higher spatial diversity,enhancedmultiplexing gain,and more flexible beamforming,become more appealing in 6G near-fieldcommunications because the spherical wave-based model renders more substantial channel variation inthe spatial domain 57-59.Furthermore,distributed MAs can be seamlessly

224、 integrated into cell-freecommunication systems,providing additional DoFs in antenna position and/or rotation for improvingthe performance of 6G networks.In wireless sensing and ISAC applications for 6G,the MA systemscan effectively enlarge the antenna aperture such that the angular/ranging accuracy

225、 is increasedmanifoldly.For sufficiently large antenna moving regions,the MA-aided systems can realizesuper-resolution for near-field sensing.In summary,MAs opened up a new direction for research andengineering practice in 6G near-field communication and/or sensing.More collective efforts intheoreti

226、calresearch,technicalexploration,systemdesign,experimentalverification,andstandardization activities are required to unleash the full potential of MAs in future 6G networks.2.3 Integrated Sensing and CommunicationIn addition to high-capacity communication,the next generation of wireless networks als

227、o has thepotential to achieve high-precision perception.Therefore,the ISAC technology has also attractedwidespread research interest in academia and industry 60.Compared with traditional wirelesspositioning and channel estimation,wireless perception relies on the echo signal reflected by passive14/2

228、32targets,rather than the pilot signal sent by active devices.Currently,many existing modulationwaveforms have been proven to be applicable to wireless sensing,such as orthogonal frequencydivision multiplexing(OFDM)and orthogonal time frequency space(OTFS),indicating that sensingfunctions can be sea

229、mlessly integrated into existing wireless communication networks6162.Besides,novel dual-functional waveform design strategies have been developed to balance thecommunication and sensing performances under different application scenarios,e.g.,ISAC atmmWave/THz frequencies or communication/sensing-cen

230、tric ISAC services.In far-field sensing,increasing the size of the antenna array often only improves the resolution ofangle estimation,while the resolution of distance and velocity mainly depends on signal bandwidth andperception duration.However,in the near-field region,the propagation of spherical

231、 waves allowslarge-scale antenna arrays able to estimate the distance and movement speed between objects.On theone hand,even within a limited bandwidth,near-field channels can still effectively contain distanceinformation,improving the resolution of distance estimation in narrowband systems.On the o

232、ther hand,the estimation of target velocity depends on the estimation of Doppler frequency.Compared withfar-field sensing,near-field sensing may have significantly different Doppler frequencies whenobserving two antennas in a large-scale antenna array from different directions,which can enhance thee

233、stimation of object movement speed 4063,as shown in Fig.2.10 Based on the above discussion,near-field effects have the potential to promote high-precision perception in situations wheretime-frequency resources are limited.Therefore,near-field synesthesia integration is a highlypromising technology.A

234、 recent survey on the opportunities and challenges of ISAC in the near fieldcan be found in 64.Fig.2.10 Near field ISAC system402.4 Wireless PositioningIn traditional far-field communication systems,the angle and distance information of the targetrelative to the receiving point is mainly obtained by

235、 estimating the arrival angle and time of the signalat the target based on the assumption of plane waves 65.The far-field communication system needsto deploy multiple receiving points as positioning anchors to estimate the three-dimensionalcoordinates of the target based on the angle and distance in

236、formation of multiple anchors.In order toobtain more accurate angle and distance information,far-field communication systems usually need toconfigure measurement signals with larger bandwidth.In addition to the use of distance and angle,the15/232use of the characteristics of the received signal as a

237、 fingerprint for localization is also a commonmethod of localization and has been studied in far-field communications 66.In the near field,basedon the spherical wave model,the arrival angles of signals from antenna units in different regions of theantenna array at the target are different.By utilizi

238、ng the signal transmission characteristics of beamconvergence,near-field communication systems locate targets through the differences in channelangles in different areas of the antenna array,thereby reducing the demand for measurement signalbandwidth 66.Meanwhile,the deployment of large-scale antenn

239、a arrays is beneficial for furtherenhancing angular resolution and providing additional distance resolution in the near-field region,which is conducive to achieving high-precision positioning in 6G mobile communication 67.Fig.2.11 High precision positioning based on near-field effectsHigh precision

240、positioning services can be provided in the near-field through various forms such asELAA,RIS,and distributed MIMO(D-MIMO).The positioning process of near-field communication systems is different from traditional far-fieldcommunication systems in terms of signal system,channel model,and positioning p

241、rinciple.The twobelong to heterogeneous positioning networks.Therefore,heterogeneous positioning network fusionalgorithms are needed between far-field and near-field communication systems to ensure seamlesspositioning services 6970.The fusion of heterogeneous positioning networks relies on theimplem

242、entation of positioning accuracy estimation algorithms 7172.For regional positioningsystems,including near-field communication systems,positioning accuracy algorithms can evolve intoavailability estimation 73 to support two different modes of interoperability between heterogeneouspositioning systems

243、:soft fusion and hard switching 74.16/232Fig.2.12 RIS enabled near-field localization:(a).System Setup(b).Coordinate SystemIn the field of wireless communication networks,radiolocation technology,as a viable alternativeto obtain user location data in environments where GPS signals are unavailable,no

244、t only improves thecommunication speed,but also enhances the positioning accuracy with each generation of mobilecommunication technology.In 4G systems,the user estimates the distance to each BS from thetime-of-arrival(ToA),and the location is determined by using at least four Line-of-Sight(LoS)BSs o

245、fthe TDoA measurements to determine the 3D position.In 5G systems,both the BS and the user deviceare equipped with multiple antennas that can simultaneously take into account the delay and angle ofthe signal propagation,improving the positioning accuracy and making it possible to efficiently locatet

246、he device using only one BS.With the growth of applications such as smart factories,autonomous driving,and augmentedreality,there is an increasing demand for higher positioning accuracy in 5G and 6G networks.5Gswide bandwidth and large antenna arrays further enhance positioning accuracy.However,when

247、 5G and6G systems operate in the high frequency mmWave and THz bands,the connections between devicesare susceptible to obstacles propagation is critical for accurate position estimation,and existingpositioning methods will generate significant estimation errors if the LoS link is obstructed.RIS prov

248、ides reliable and highly accurate location estimation capability at low cost and with highenergy efficiency75-77.When the LoS link is obstructed,the RIS can establish a virtual LoS linkand perform accurate delay measurements using broadband signals78.Unlike non-reconfigurablescatterers in convention

249、al environments,RIS can adjust the phase offset of its reflective units,resultingin significant beamforming gains.In addition,RIS has many elements that contribute to the resolutionduring positioning,enhancing the AoA for uplink or AoD positioning resolution for downlink79.For communication and posi

250、tioning applications involving RIS,accurate and well-defined RISphase control is essential.This requires the development of appropriate RIS phase control models thatconsider multiple factors such as mutual coupling8081,calibration82,quantization and power lossper element83.Most of the existing studi

251、es have focused on ideal phase shifters,ignoring the effects,which are crucial for the effectiveness and reliability of RIS-based positioning methods8485.There are two main modes of RIS-based localization techniques:receiving mode 85 andreflecting mode8687.In the receiving mode,a large smart surface

252、 is used to determine the positionof the user in front of it for both near-field and far-field scenarios.In reflection mode,the receivedsignal strength(RSS)at different points is enhanced by adjusting the RIS reflection coefficients toimprove the positioning accuracy86.However,these models may not b

253、e accurate enough whendealing with large surfaces and arrays,especially when the mobile device is in the Fresnel regioninstead of the Fraunhofer region.In the Fresnel region,the wavefront curvature is significant and17/232cannot be approximated as a plane wave,and ignoring the spherical wavefront mi

254、sses criticalinformation about the position and orientation of the mobile device.Under the spherical wavefront channel model,87 investigated 3D localization using a simplifiedRIS lens design to solve the RIS-assisted geometric near-field localization problem in the presence ofLoS blocking and presen

255、ted a Fisher information analysis and a closed form expression for the Fisherinformation matrix(FIM),showing the dependence of the position error bound(PEB)on the RIS phaseprofile.Three RIS phase profiles(random,directional and positional configurations)are used todemonstrate the role of RIS in near

256、-field regional localization and communication.These profilesconsider the magnitude and phase response of the RIS,using a realistic phase-dependent magnitudemodel.The random profiles provide a uniform signal-to-noise ratio(SNR)over the deployment area,while the directional and positional profiles in

257、crease the SNR near the users location.2.5 Simultaneous Wireless Information and Power TransferIn near-field communication,a highly directional point beam can be achieved,which concentratesthe target area of the beam near the target device,thereby concentrating the energy of the RF signal tothe ener

258、gy collection node of the Internet of Thing(IoT)devices.By utilizing the large number ofantennas and high-precision position information,the efficiency of wireless energy transmission can besignificantly improved,reducing energy waste during the transmission process.The near-field beamfocusing chara

259、cteristics limit the spread of energy to undesired location,but does not affect theefficiency on its own.In indoor scenarios or scenarios where the size of BS antennas is limited,wireless communication systems can use intelligent meta-surfaces to construct near-field channels andgather signal energy

260、 from home BSs to energy harvesting nodes.In addition,in the near-fieldcommunication system,the super large antenna array can obtain higher spatial resolution in thenear-field range based on the wireless channel of spherical wave model,so that the BS can supporthigher density simultaneous wireless i

261、nformation and power transfer(SWIPT)terminals.SWIPTallows devices to harvest energy from RF waves and convert it to electrical energy,storing that energyinto the devices battery,maximizing the devices lifespan and representing a new solution to limitedenergy8889.Fig.2.13 Schematic diagram of near-fi

262、eld wireless energy transmission18/2322.6 Physical Layer SecurityDue to the natural broadcasting and mobile characteristics of wireless communication,thecommunication of legitimate users in the network is easily eavesdropped and attacked by illegal users,and secure transmission has always been an im

263、portant issue in wireless communication.In far-fieldcommunication,if the eavesdropper is in the same direction as the legitimate user,especially when theeavesdropper is closer to the BS,secure transmission will be difficult to achieve.Unlike the directionalfocusing of beamforming in far-field commun

264、ication,in near-field communication assisted by ultralarge arrays,the beams formed by BSs have strong positional focusing 90.This property allows theenergy of the transmitted signal to concentrate at the location of legitimate users rather than just in theirdirection,effectively reducing information

265、 leakage at the location of eavesdropping users andimproving the systems secure channel capacity.By optimizing the beam focusing design of the BS,thepotential of near-field communication in enhancing physical layer security can be fully explored.Fig.2.14 Left:Far field secure communication using bea

266、m steering.Right:Near field securecommunication using beam focusing.2.7 Multiple AccessMultiple access technologies leverage time,frequency,code,and space domains in order toachieve the efficient utilization of transmission resources,which is the key to improving thetransmission rate of the next-gen

267、eration wireless network.In the current 5G massive MIMO system,spatial division multiple access(SDMA)utilizes orthogonal resources in the angular domain todistinguish different users;non-orthogonal multiple access(NOMA)further allows multiple users toreuse the same resource block and mitigate the in

268、ter-user interference through the power or codedomain;unsourced random access introduces a competition mechanism of access resources(such ascodewords),which saves the resource overhead intended for random access with short data packages inthe scenario of massive machine-type communications(mMTC).Com

269、pared with the far-field transmission model applied in multiple access technologies such asSDMA,the near-field transmission model reveals the two-dimensional beam focusing characteristics inthe angle-distance domain and yields a larger spatial degree of freedom(DoF),indicating a greatincrease in ava

270、ilable spatial resources.Therefore,integrating near-field characteristics into the designof multi-access technologies would be more conducive to serving the access requirements of massiveusers and further improving the system spectrum efficiency.19/232Fig.2.15 Schematic diagram of near-field multipl

271、e access2.8 Massive IoT CommunicationsMassive IoT communications refer to the networking infrastructure and protocols required tosupport the connectivity needs of a large number of IoT devices.These devices typically generate andtransmit small amounts of data intermittently,often with low power cons

272、umption requirements.In sucha scenario,massive control type interactions are the dominant communication scenario.These controltype communications are mostly with short packet that is even shorter than the signaling data length,but of a massive number.The communication infrastructure must be capable

273、of supporting a massivenumber of IoT devices,potentially numbering in the millions or even billions.This requires scalablenetwork architectures,protocols,and management systems that can efficiently handle the increasedtraffic and device density.On the other hand,some high concurrent communications m

274、ight happenwith even faster transmission speed requirements,for instance,while working together to deliver areal-time industrial situation awareness to the control center in the industrial IoT applications.Byaddressing these requirements,communication infrastructure needs to effectively support thec

275、onnectivity needs of massive IoT deployments,enabling a wide range of applications and unlockingthe full potential of IoT technology.ELAA-based Near-field technology is a potential player for massive IoT communications in 6G,especially for the massive connectivity and high-speed concurrent communica

276、tion requirements.Byincorporating the ELAA,the BS can connect more IoT devices within the range of the Rayleighdistance.Moreover,the ELAA system working on the near-field range will be an ideal solution to thehigh-speed concurrent communications.According to a prior study 91,arbitrary signal to nois

277、e ratio(SNR)can be achieved simply by increasing the transceiver number,which yields faster transmissionspeed while deployed.2.9 On-chip Wireless CommunicationsOn-chip Wireless Communications refers to the utilization of on-chip antennas or near-fieldcoupling,among other wireless interconnect method

278、s,to facilitate data exchange and wirelesscommunication between different modules within or among chips.The transmission distance ofon-chip wireless communication typically falls below 1cm,offering advantages such as low loss,hightransmission rates,and high integration.The applications of on-chip co

279、mmunication are widespread,notably in the IoT domain,enabling seamless interconnection among smart chips,devices,andwearables,thereby significantly enhancing communication efficiency and reducing wiring complexity.20/232However,this technology faces limitations including increased chip area costs,se

280、curity and privacyconcerns,and increased power consumption.Nevertheless,as the operating frequency rises to themmWave/THz frequency bands,the size of on-chip antennas significantly reduces,leading to asubstantial decrease in chip area 9293 Moreover,the proximity between transceiver chips reduces,the

281、reby relaxing the signal power requirements for wireless communication.Additionally,the highfrequency signals exhibit good directionality,enhancing the security and reliability of informationtransmission.These factors substantially alleviate the design complexity of on-chip wirelesscommunication sys

282、tems94.Simultaneously,compared to traditional wired communication method between chips,utilizingwireless communication can avoid the drawbacks introduced by transmission lines,such as highlatency,high crosstalk,limited bandwidth,and parasitic effects.As illustrated in Fig.2.16,byintegrating on-chip

283、antennas into the chip,the signal transmission mode shifts from traditional wiredtransmission to on-chip and inter-chip wireless communication,forming a flexible architecture ofon-chip networks,thereby avoiding the limitations of wired interconnection layouts95.Fig.2.16 Utilizing on-chip and inter-c

284、hip communication with antennasAdditionally,on-chip wireless communication plays a significant role in signal transmissionbetween different system-level chiplets.As illustrated in Fig.2.17,when forming a System-on-a-Chip(SoC)through heterogeneous integration,the difficulty of high-frequency intercon

285、nection betweenchiplets arises due to different structures and semiconductor materials.Traditional wire bondingmethods severely degrade signal integrity.By utilizing on-chip wireless communication,high-speed,high-bandwidth heterogeneous integration between chiplets can be achieved,effectively enhanc

286、ing theversatility of large-scale heterogeneous systems96.Fig.2.17 Wireless interconnection between chips with different semiconductor materialsFurthermore,as on-chip wireless communication exploits the radiation effects of on-chip antennasor near-field coupling,it is no longer constrained by the on

287、e-to-one data transmission mode oftraditional wired interconnection.In terms of data transmission,it offers higher flexibility andadaptability,enabling one-to-many transmission to support high-density device connections and data21/232exchanges among massive devices,thus providing more feasibility fo

288、r the design of single-chipmulti-core processors,as illustrated in Fig.2.18 97.Fig.2.18 Single-chip multi-core processors utilizing on-chip wireless communication technologyIn conclusion,on-chip wireless communication can be widely applied in various mobile devicesand embedded systems,such as smartp

289、hones,tablets,smartwatches,IoT devices,etc.,to enhance theperformance of communication devices.The application of on-chip wireless communication in 6G canreduce communication latency,provide faster data transmission capabilities,and enhance the real-timeperformance and energy efficiency of communica

290、tion systems.High-speed and efficient on-chipcommunication provide feasible solutions for future 6G wireless communication systems,terahertzintegrated circuits,and chip-to-chip communication interconnections.22/2323 Fundamental Theories of Near-fieldWith the technological evolution from 5G to 6G com

291、munication,in order to further improvebeamforming performance and communication rates,larger antenna array apertures and highercommunication frequencies are being employed.However,larger arrays also bring many traditionallyfar-field communication scenarios into the electromagnetically-defined near-f

292、ield communicationrange.In near-field communication,the electromagnetic waves used for information transmission canonly be regarded as spherical waves instead of plane waves.This emerging physical characteristic isinevitable and gives rise to many new electromagnetic effects,such as spatial non-stat

293、ionarity,polarization,and evanescent waves.As a result,many traditional communication algorithms that wereexplicitly designed for far-field operation suffer from severe performance degradation or fail toleverage the new features for optimal performance in 6G near-field scenarios12.In this chapter,st

294、arting from the definition of near-field provided by electromagnetic theory,we analyze the near-fieldelectromagnetic effects,explaining their sources and impacts on existing systems.Furthermore,basedon the existing literature on near-field communication,we summarize the changes in communicationsyste

295、m design and performance caused by the emergence of near-field effects,focusing primarily oncommunication DoFs and communication capacity.The foundational theory of near-field comprises four main parts:electromagnetic near-fielddefinition,near-field electromagnetic properties and physical effects,th

296、eoretical analysis of near-fieldcommunication DoFs and near-field performance analysis.3.1 Near-field Range PartitioningIn this section,we first introduce the differences between far-field and near-field communications.Then,we establish principles for determining the boundaries of far-field and near

297、-field regions inseveral typical application scenarios.As shown in Fig.3.1,based on electromagnetic theory and antenna theory,the fields around atransmitter can be divided into the near-field and far-field regions,with the near-field further classifiedinto the reactive near-field region and the radi

298、ative near-field region98.The reactive near-fieldregion is limited to the space close to the antenna(within the Fresnel distance),where evanescentwaves dominate,and the electromagnetic field does not propagate from the antenna in the form ofradiative waves.The radiative near-field region extends sev

299、eral wavelengths away from the antenna(between the Fresnel distance and the Rayleigh distance).The Rayleigh distance is also known asFraunhofer distance.In this region,the amplitude differences between electromagnetic waves ondifferent antennas within the array are not significant,but the phase chan

300、ges exponentially with theindex of the antennas.The signal propagation model in this region must be modeled using a sphericalwave model.The far-field region surrounds the radiative near-field region,and in the far-field,electromagnetic waves can be approximated as plane waves.Since the reactive near

301、-field region isusually small and evanescent waves decay exponentiallywith distance,practical near-fieldcommunication systems primarily focus on wireless communication within the radiative near-fieldregion,where near-field generally refers to the radiative near-field region.23/2323.1.1 Characterizin

302、g Near-Field and Far-Field Boundaries:Perspectives andEmpirical RulesIn existing research,there are multiple perspectives and empirical rules to characterize theboundaries between the near-field and far-field regions,mainly including phase difference,powerdifference,channel capacity,and localization

303、 error.(1)Phase difference perspectiveFrom the perspective of phase difference,the classic boundary between near-field and far-field isreferred to as the Fraunhofer distance or Rayleigh distance 99(considering a maximum phasedifference between spherical wave and plane wave model not exceeding8),expr

304、essed as22,where represents the maximum aperture of the antenna,and represents the wavelength of the carrier.If thedistance between the user and the BS is greater than the Rayleigh distance,the user can be consideredto be in the far-field region.In this region,the signal propagation can be approxima

305、ted as plane waves.On the other hand,if the distance between the user and the BS is smaller than the Rayleigh distance,the user can be considered to be in the near-field region.Fig.3.1 Far-field plane wavefront and near-field spherical wavefront and corresponding physical spacenormalized received en

306、ergyPlane waves differ from spherical waves in their ability to adjust to radiant energy from space.More precisely,plane waves are far-field approximations of spherical waves.In the far-field region,thephase of an electromagnetic wave can be approximated by a Taylor expansion in terms of a linearfun

307、ction of the antenna exponent.This clean linear phase forms a plane wavefront that is only relatedto the angle of incidence.Thus,using the plane wavefront,far-field beamforming can steer the beamenergy to specific angles at different distances,which is also known as beam steering.Unfortunately,this

308、clean linear phase does not completely reveal information about the spherical wave.In thenear-field region,the phase of a spherical wave should be derived accurately based on the physicalgeometry,which is a nonlinear function of the antenna index.The information on the angle ofincidence and the dist

309、ance for each path between the BS and the user equipment(UE)is contained inthis nonlinear phase.By utilizing the additional distance information of the spherical wavefront,near-field beamforming can focus the beam energy at a specific location and achieve energy focusing24/232in both the angle and d

310、istance domains.Based on this property,near-field beamforming is also knownas beam focusing.The primary concept behind the derivation of the Rayleigh distance is as follows 98.The truephase of an electromagnetic wave must be calculated based on an accurate spherical wavefront modeland the BS antenna

311、 position.In the far-field case,this phase is usually approximated by a first-orderTaylor expansion based on a planar wavefront model.This approximation results in a phase differencewhich increases with decreasing distance.The distance between the center of the BS array and thecenter of the UE array

312、 is defined as the Rayleigh distance when the maximum phase differencebetween all BS and UE antennas reaches 8.Therefore,if the communication distance is shorter thanthe Rayleigh distance,the maximum phase difference will be greater than 8.In this case,thefar-field approximation becomes inaccurate,s

313、o it is necessary to utilize the near-field propagationmodel.Basedonthisdefinition,thenear-fieldrangesofsingle-input-multiple-output(SIMO),multiple-input-single-output(MISO),and multiple-input-multiple-output(MIMO)communicationsystems can be obtained.As shown in Fig.3.2,the near-field range for SIMO

314、/MISO scenarios isaccurately determined by the classical Rayleigh distance,which is proportional to the square of theaperture of the BS array.For the MIMO scenario,both the BS array aperture and the UE array aperturecontribute to the Rayleigh distance since ELAA is employed on both sides of the BS-U

315、E link;i.e.,thenear-field range is proportional to the square of the sum of the BS array aperture and the UE arrayaperture.For RIS systems,the cascaded BS-RIS-UE channel consists of BS-RIS and RIS-UE links.Therefore,the 8maximum phase difference needs to be calculated by summing the BS-RISdistances

316、and the RIS-UE distances when calculating the phase difference,and the near-field range ofthe RIS system is determined by the harmonic mean of the BS-RIS distances and the RIS-UE distances,as shown in Fig.2.It can be further seen from Fig.3.2 that RIS-assisted communication operates in thenear-field

317、 region as long as either of these two distances is shorter than the Rayleigh distance.Therefore,near-field propagation is more likely to occur in RIS systems 35.Fig.3.2 Near-field range for typical communication scenarios(2)Power difference perspectiveWhen using the optimal Maximum Ratio Combining(

318、MRC),signals from different antennaelements can be completely aligned in phase,thus eliminating the impact of phase differences onreceived power.However,the MRC combining weights derived from classical far-field planarwavefront cannot perfectly compensate for the phase differences of near-field sphe

319、rical wavefront,leading to severe power losses.Based on this observation,reference 100 quantatively analyzes therelationship between the focal point of incident spherical wavefront and the induced power loss.Then,an effective Rayleigh distance is proposed to capture the physical region where the pow

320、er loss is25/232greater than a threshold.This new metric allows a much more accurate characterization of theboundary of near-field region than the traditonal Rayleigh distance.After eliminating the effect of signal phase on received power through MRC,the received poweris only determined by the ampli

321、tude response differences of the antenna elements at the receiver.Considering the amplitude response differences of different antenna elements on the same transmitterantenna array,the Critical Distance and Uniform Energy Distance is proposed 101102,whichcharacterize the near-field range from the per

322、spective of power differences between different antennaelements.That is,the power ratio between the weakest and strongest antenna elements detected at thereceiver exceeds a specified threshold beyond this distance.The Critical Distance is determined by theantenna aperture,primarily characterizing th

323、e boundary of the field near the antenna aperture axis.TheUniform Energy Distance further considers factors such as array structure and the projected aperture ofthe antenna array,providing a more accurate description of the near-field boundary in off-axis regions.Looking at the differences in receiv

324、ed power between the plane wave channel model and thespherical wave channel model from another perspective,reference 103 has derived the equal-powerlines and equal-power surface for the near-field region based on the uniform linear array(ULA)and theuniform circular planar array(UCPA)structures respe

325、ctively,characterizing the near-field range.(3)Capacity perspective:From the perspective of capacity,the near-field range can be described by combining the channelcapacity 104,eigenvalues 105,rank 103,multi-stream transmission characteristics 106,oreffective DoFs 107,to evaluate the applicable area

326、of far-field plane waves and near-field sphericalwaves.Reference 103 proposes the boundary of the near-field region through equip-rank surface.Itshows that the near-field range increases with the number of scatterers in both LoS andnon-line-of-sight(NLoS)environments,with is more apparent in the NLo

327、S environment.Consideringspatial reuse,reference 106 introduces the effective reuse distance metric(),representing themaximum distance at which the channel can efficiently accommodate independent spatial streamsat a specific signal-to-noise ratio(SNR).The near-field boundary from the perspective of

328、multi-streamtransmission is discussed by combining the channels effective DoFs 107,which demonstrates that thenear-field range is not only related to the antenna array aperture but also influenced by the number ofantenna elements.(4)Localization error perspectiveThe Fraunhofer distance serves merely

329、 as a rough estimate to delineate between the far-field andnear-field regions.However,for localization applications,it is not an appropriate boundary betweennear and far fields,as it does not consider several essential parameters for localization,such as AoA,beam squint,or transmit power.In fact,it

330、has been shown in 108 that this distance is insufficient tosuggest when the mismatched far-field model can be used in practice,instead of the more accuratenear-field,without significant performance degradation due to the model mismatch.Therefore,a metricbased on the mismatched Cramer-Rao lower bound

331、 is proposed,such that the boundary between thefar-field and the near-field is the-3 dB contour of the model mismatch positioning error between thetwo regimes.(5)Mathematical model perspectiveExisting literatures on near-field communications usually assume that the antenna elements areeither isotrop

332、ic or simply characterized by the projected aperture.In practice,individual antenna26/232element is also directional in general,i.e.,the signal power of each antenna element depends on itslocally observed angle of arrival/departure(AoA/AoD),which may render commonly used methodsunjustified.Therefore

333、,reference 109 introduces the normalized difference between the near-fieldNUSW model and far-field UPW model considering the directivity of each array element,whichaccounts for both the phase and amplitude simultaneously.Based on such metric,closed-formexpressions of the near-field and far-field boundary are obtained by using the commonly usedcosine-based directional gain pattern in antenna theory