5G网络技术架构
主要讲述当前网络挑战与发展趋势，以及网络总体设计和5G网络的功能特是哪个，和关键技术 。最后总结和展望未来AT-2◇21MT202056/推进组5G无线技术架构白皮书引言在过去的三十年里,移动通信经历了从语音多址技术之外,大规模天线、超密集组网和全频业务到移动宽带数据业务的飞跃式发展,不仅深谱接入都被认为是5G的关键使能技术。此外,新刻地改变了人们的生活方式,也极大地促进了社型多载波、灵活双工、新型调制编码、终端直通会和经济的飞速发展。移动互联网和物联网作为(2)、全双工(又称同时同频全双工)等也未来移动通信发展的两大上要驱动力,为第五代是潜在的5G无线关键技术。5G系统将会构建在以移动通信(5G)提供了广阔旳应用前景。面向新型多址、大规模天线、超密集组网、全频谱接2020年及未来,数据流量的千倍增长,千亿设备入为核心的技术体系之上,全面满足面向2020年连接和多样化的业务需求都将对5G系统设计提出及未来的5G技术需求。严峻挑战。与4G相比,5G将支持更加多样化的当前,5G愿景与需求已基明确,概念与技场景,融合多种无线接入方式,并充分利用低频术路线逐步清晰,国际标准制定工作即将启动。和髙频等频谱资源。同时,5G还将满足网络灵为此,迫切需要尽快细化5G技术路线,整合各种活部署和高效运营维护的需求,大幅提升频谱效无线关键技术,形成5G无线技术框架并推动达成率、能源效率和成本效率,实现移动通信网络的产业共识,以指导5G国际标准及后续产业发展。可持续发展传统的移动通信升级换代都是以多址接入技术为主线,5G的无线技术创新来源将更加丰富。除了稀疏码分多址(SCMA)、图样分割多址(PDMA)、多用户共享接入(MUSA)等新型AT-292◇MT2020(5G)推进组5G无线技术架构白皮书场景与技术需求与以往移动通信系统相比,5G需要满足更加低功耗大连接场景主要面向环境监测、智多样化的场景和极致的性能挑战。归纳未来移动能农业等以传感和数据采集为目标的应用互联网和物联网主要场景和业务需求特征,可提场景,具有小数据包、低功耗、低成本炼出连续广域覆盖、热点高容量、低时延高可靠海量连接的特点,要求支持百万/平方公里和低功耗大连接四个5G主要技术场景。连接数密度。·连续广域覆盖玚景是移动通信最基本的覆总之,5G的技术挑战主要包括盖方式,在保证用户移动性和业务连续性0.1-1GbDs的用广体验速率,数十Gbps的的前提下,无论在静止还是高速移动,覆峰值速率,数十Tbυs/km的流量密度,1盖中心还是覆盖边缘,用户都能够随时随百万/平方公里的连接数密度,毫秒级的地获100Mbps以上的体验速率。端到端时延,以及百倍以上能效提升和单热点高容量场景:要面向室内外局部热点位比特成本降低。区域,为用户提供极高的数据传输速率,满足网络极高的流量密度需求。要技术挑战包括 1Gbps用户休验速率、数|Gbps峰值速率和数十Tbps/km的流量密度低时延高可靠场景主要面向车联网、工业控制等物联网及垂直行业的特殊应用需求,为用户提供亳秒级的端到端时延和或接近100%的业务可靠性保证。AT-2◇2M-2020(56推进组5G无线技术架构白皮书5G无线技术路线面对5G场景和技术需求,需要选择合适的5G将通过工作在较低频段的新空口来满足无线技术路线,以指导5G标進化及产业发展。大覆盖、高移动性场景下的用户体验和海量设综合考虑需求、技术发展趋势以及网络平滑演备连接。同时,需要利用高频段丰富的频谱资进等因素,5G空口技术路线可由5G新空口(含源,来满足热点区域极高的用户体验速率和系低频空口与高频空口)和4G演进两部分组成。统容量需求。综合考虑国际频谱规划及频段传LTE/LTE一 Advanced技术作为事实上的播特性,5G应当包含工作在6GHz以下频段的统一4G标准,已在全球范围内大规模部署。为低频新空口以及工作在6GHz以上频段的高频了持续提升4G用户体验并支持网络平滑演进新空口。需要对4G技术进一步增强。在保证后向兼容的5G低频新空口将采用全新的空口设计,前提下,4G演进将以LE/LTE- advanced技引入大规模天线、新型多址、新波形等先进技术梹架为基础,在传统移动通信频段引入增强术,攴持更短的帧结构,更精简的信令流程,技术,进一步提升4G系统的速率、容量、连接更灵活的双工方式,有效满足广覆盖、大连接数、时延等空口性能指标,在一定程度上满足及髙速等多数场景下的体验速率、时延、连接5G技术需求。数以及能效等指标要求。在系统设计时应当构受现有4G技术框架的约東,大规模天线建统一的技术方案,通过灵活配置技术模块及超密集组阏等增強技术的潜力难以完仝发挥,参数来淸足不同场景差异化的技术需求。全频谱接入、部分新型多址等先进技术难以在5G高频新空口需要考虑高频信道和射频器现有技术框架下采用,4G演进路线无法满足5G件的影响,并针对波形、调制编码、天线技术极致的性能需求。因此,5G需要突破后向兼容等进行相应的优化。同时,高频频段跨度大的限制,设计全新的空口,充分挖掘各种先进候选频段多,从标准、成本及运维角度考虑,技术的潜力,以全面满足5G性能和效率指标要应当尽可能采用统一的空口技术方案,通过参求,新空口将是5G主要的演进方向,4G演进将数调整来适配不同信道及器件的特性。是有效补充。髙频段覆盖能力弱,难以实现全网覆盖,需要与低频段联合组网。由低频段形成有效的3AT-22MT2020(5G)推进组5G无线技术架构白皮书网络覆盖,对用户进行控制、管理,并保证基本的数据传输能力;高频段作为低频段的有效补充,在信道条件较好情况下,为热点区域用户提供高速数据传输。5G无线技术路线主要场景连续广域覆盖6-100GHZ5G高频新空口冷热点高容量5G低频新空口低时延高可靠
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MATLAB在卡尔曼滤波器中应用的理论与实践Kalman
MATLAB在卡尔曼滤波器中应用的理论与实践KalmanKALMAN FILTERINGTheory and Practice Using MATLABThird editionMOHINDER S GREWALCalifornia State University at FullertonANGUS P. ANDREWSRockwell Science Center (retired)WILEYA JOHN WILEY & SONS, INC. PUBLICATIONCopyright 2008 by John Wiley sons, Inc. All rights reservedPublished by John Wiley sons, InC, Hoboken, New JerseyPublished simultaneously in CanadaNo part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or byany means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permittedunder Section 107 or 108 of the 1976 United States Copyright Act, without either the prior writtenpermission of the Publisher, or authorization through payment of the appropriate per-copy fee to theCopyright Clearance Center, Inc, 222 Rosewood Drive, Danvers, MA 01923,(978)750-8400, fax(978)750-4470,oronthewebatwww.copyright.com.RequeststothePublisherforpermissionshouldbe addressed to the Permissions Department, John Wiley Sons, Inc, lll River Street, Hoboken, NJ07030,(201)748-6011,fax(201)748-6008,oronlineathttp://www.wiley.com/go/permissionimit of liability Disclaimer of Warranty: While the publisher and author have used their best efforts inpreparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability orfitness for a particular purpose. No warranty may be created or extended by sales representatives orwritten sales materials. The advice and strategies contained herein may not be suitable for your situationYou should consult with a professional where appropriate. Neither the publisher nor author shall be liablefor any loss of profit or any other commercial damages, including but not limited to special, incidentalconsequential, or other damagesFor general information on our other products and services or for technical support, please contact ourCustomer Care Department within the United States at(800)762-2974, outside the United States at(317)572-3993 or fax(317)572-4002Wiley also publishes its books in a variety of electronic formats. Some content that appears in print maynot be available in electronic format. For more information about wiley products, visit our web site atwww.wiley.comLibrary of Congress Cataloging- in-Publication DataGrewal. Mohinder sKalman filtering: theory and practice using MATLAB/Mohinder S. GrewalAngus p. andrews. 3rd edIncludes bibliographical references and indexISBN978-0-470-17366-4( cloth)1. Kalman filtering. 2. MATLAB. I. Andrews, Angus P. II. TitleQA402.3.G69520086298312—dc22200803733Printed in the United States of america10987654321CONTENTSPrefaceAcknowledgmentsXIIIList of abbreviationsXV1 General Information1.1 On Kalman Filtering1.2 On Optimal Estimation Methods, 51. 3 On the notation Used In This book 231. 4 Summary, 25Problems. 262 Linear Dvnamic Systems2. 1 Chapter focus, 312.2 Dynamic System Models, 362. 3 Continuous Linear Systems and Their Solutions, 402.4 Discrete Linear Systems and Their Solutions, 532.5 Observability of Linear Dynamic System Models, 552.6 Summary, 61Problems. 643 Random Processes and Stochastic Systems3.1 Chapter Focus, 673.2 Probability and random Variables (rvs), 703.3 Statistical Properties of RVS, 78CONTEN3.4 Statistical Properties of Random Processes(RPs),803.5 Linear rp models. 883.6 Shaping Filters and State Augmentation, 953.7 Mean and Covariance propagation, 993.8 Relationships between Model Parameters, 1053.9 Orthogonality principle 1143.10 Summary, 118Problems. 1214 Linear Optimal Filters and Predictors1314.1 Chapter Focus, 1314.2 Kalman Filter. 1334.3 Kalman-Bucy filter, 1444.4 Optimal Linear Predictors, 1464.5 Correlated noise Sources 1474.6 Relationships between Kalman-Bucy and wiener Filters, 1484.7 Quadratic Loss Functions, 1494.8 Matrix Riccati Differential Equation. 1514.9 Matrix Riccati Equation In Discrete Time, 1654.10 Model equations for Transformed State Variables, 1704.11 Application of Kalman Filters, 1724.12 Summary, 177Problems. 1795 Optimal Smoothers5.1 Chapter Focus, 1835.2 Fixed-Interval Smoothing, 1895.3 Fixed-Lag Smoothing, 2005.4 Fixed-Point Smoothing, 2135.5 Summary, 220Problems. 226 Implementation Methods2256. 1 Chapter Focus, 2256.2 Computer Roundoff, 2276.3 Effects of roundoff errors on Kalman filters 2326.4 Factorization Methods for Square-Root Filtering, 2386. 5 Square-Root and UD Filters, 2616.6 Other Implementation Methods, 2756.7 Summary, 288Problems. 2897 Nonlinear Filtering2937.1 Chapter Focus, 2937.2 Quasilinear Filtering, 296CONTENTS7.3 Sampling Methods for Nonlinear Filtering, 3307.4 Summary, 345Problems. 3508 Practical Considerations3558.1 Chapter Focus. 3558.2 Detecting and Correcting Anomalous behavior, 3568.3 Prefiltering and Data Rejection Methods, 3798.4 Stability of Kalman Filters, 3828. 5 Suboptimal and reduced- Order Filters, 3838.6 Schmidt-Kalman Filtering, 3938.7 Memory, Throughput, and wordlength Requirements, 4038.8 Ways to Reduce Computational requirements 4098.9 Error Budgets and Sensitivity Analysis, 4148.10 Optimizing Measurement Selection Policies, 4198.11 Innovations analysis, 4248.12 Summary, 425Problems. 4269 Applications to Navigation4279.1 Chapter focus, 4279.2 Host vehicle dynamics, 4319.3 Inertial Navigation Systems(INS), 4359. 4 Global Navigation Satellite Systems(GNSS), 4659.5 Kalman Filters for GNSS. 4709.6 Loosely Coupled GNSS/INS Integration, 4889.7 Tightly Coupled GNSS /INS Integration, 4919. 8 Summary, 507Problems. 508Appendix A MATLAB Software511A 1 Notice. 511A 2 General System Requirements, 511A 3 CD Directory Structure, 512A 4 MATLAB Software for Chapter 2, 512A. 5 MATLAB Software for Chapter 3, 512A6 MATLAB Software for Chapter 4, 512A. 7 MATLAB Software for Chapter 5, 513A 8 MATLAB Software for Chapter 6, 513A 9 MATLAB Software for Chapter 7, 514A10 MATLAB Software for Chapter 8, 515A 11 MATLAB Software for Chapter 9, 515A 12 Other Sources of software 516CONTENAppendix b A Matrix Refresher519B. 1 Matrix Forms. 519B 2 Matrix Operations, 523B 3 Block matrix Formulas. 527B 4 Functions of Square Matrices, 531B 5 Norms. 538B6 Cholesky decomposition, 541B7 Orthogonal Decompositions of Matrices, 543B 8 Quadratic Forms, 545B 9 Derivatives of matrices. 546Bibliography549Index565PREFACEThis book is designed to provide familiarity with both the theoretical and practicalaspects of Kalman filtering by including real-world problems in practice as illustrativeexamples. The material includes the essential technical background for Kalman filter-ing and the more practical aspects of implementation: how to represent the problem ina mathematical model, analyze the performance of the estimator as a function ofsystem design parameters, implement the mechanization equations in numericallystable algorithms, assess its computational requirements, test the validity of resultsitor the filteThetant attributes ofthe subject that are often overlooked in theoretical treatments but are necessary forapplication of the theory to real-world problemsIn this third edition, we have included important developments in the implemen-tation and application of Kalman filtering over the past several years, including adaptations for nonlinear filtering, more robust smoothing methods, and develelopingapplications in navigationWe have also incorporated many helpful corrections and suggefrom ourreaders, reviewers, colleagues, and students over the past several years for theoverall improvement of the textbookAll software has been provided in MatLab so that users can take advantage ofits excellent graphing capabilities and a programming interface that is very close tothe mathematical equations used for defining Kalman filtering and its applicationsSee Appendix a for more information on MATLAB softwareThe inclusion of the software is practically a matter of necessity because Kalmanfiltering would not be very useful without computers to implement it. It provides aMATLAB is a registered trademark of The Mathworks, IncEFACEbetter learning experience for the student to discover how the Kalman filter works byobserving it in actionThe implementation of Kalman filtering on computers also illuminates some of thepractical considerations of finite-wordlength arithmetic and the need for alternativealgorithms to preserve the accuracy of the results. If the student wishes to applywhat she or he learns, then it is essential that she or he experience its workingsand failings--and learn to recognize the differenceThe book is organized as a text for an introductory course in stochastic processes atthe senior level and as a first-year graduate-level course in Kalman filtering theory andapplicationIt can also be used for self-instruction or for purposes of review by practi-cing engineers and scientists who are not intimately familiar with the subject. Theorganization of the material is illustrated by the following chapter-level dependencygraph, which shows how the subject of each chapter depends upon material in otherchapters. The arrows in the figure indicate the recommended order of study. Boxesabove another box and connected by arrows indicate that the material represented bythe upper boxes is background material for the subject in the lower boxAPPENDIX B: A MATRIX REFRESHERGENERAL INFORMATION2. LINEAR DYNAMIC SYSTEMSRANDOM PROCESSES AND STOCHASTIC SYSTEMS4. OPTIMAL LINEAR FILTERS AND PREDICTORS5. OPTIMAL SMOOTHERS6. IMPLEMENTATIONMETHODS7. NONLINEAR8. PRACTICAL9. APPLICATIONSFILTERINGCONSIDERATIONSTO NAVIGATIONAPPENDIX A: MATLAB SOFTWAREChapter l provides an informal introduction to the general subject matter by wayof its history of development and application. Chapters 2 and 3 and Appendix b coverthe essential background material on linear systems, probability, stochastic processesand modeling. These chapters could be covered in a senior-level course in electricalcomputer, and systems engineeringChapter 4 covers linear optimal filters and predictors, with detailed examples ofapplications. Chapter 5 is a new tutorial-level treatment of optimal smoothing
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