MATLAB仿真在现代通信中的应用
MATLAB仿真在现代通信中的应用,特别是无线通信仿真详细具体，适合无线通信研发人员使用。内容简介本书以现代无线电通信中的关键技术:扩频、跳频、OFDM、3G系统、无线接入系统以及常见无线通信系统作为对象,以 MATLAB/ Simulink作为仿真工具,对上述系统进行了仿真实验还对数字信号通过各种调制方式,发送滤波器后的频谱特性,以及应用各种差错控制方式后的传输特性给出了系列仿真实例。并对上述仿真内容进行了简单的原理性介绍,对于建模和仿真编程中出现的主要问题与注意事项做了相应敏讲解。仝书源程序附有光盘本书可作为高等学校通信专业的教学、科硏参考书,也可供管理部门作为参考资料、图书在版编目C|P)数据MATLAB仿真在现代通信中的应用徐明远,邵玉斌编著一西安:西安电子科技大学出版社,20114ISBN978-7-56062554-6Ⅰ①M…Ⅱ①徐…②邵…Ⅲ①通信系统一系统仿真一软件包, MATLABⅣ①TN914中国版本图书馆CP数据核字(2011第027693号策划臧延新责任编辑杨宗周出版发行西安电子科技大学出版社(西安市太白南路2号)电话(029)8824288588201467邮编70071网址www.xduph.com电子邮箱 xdupfxb00@163com经销新华书店印刷单位陕西天意印务有限责任公版次2011年4月第1版20ll年4月第1次印刷开本787毫米×1092毫米116印张13625数315千字印数1~2000册定价30.00元(含光盘)ISBN978-7-5606-25546/N·0594XDUP2846001-1煮如有印装问题可调换*本社图书封面为激光防伪覆膜,谨防盗版。前言近十年来,无线电技术突飞猛进,无线电事业快速发展。扩频、跳频、OFDM技术3G系统、蓝牙、IEEE802.11a、数字电视、智能天线等技术的出现,让人目不暇接。无线电成了现代文明的重要组成部分。人们在享受现代文明带来的方便与效率时,想到了应该对这些新技术和新系统多一些了解,然而,深奧的理论、复杂的技术、昂贵的设备与仪器给学习带来了不少的困难。人们在寻找与快速进步的技术相适应的学习与研究方法时,MATLAB仿真在新技术的学习与新系统的研发中成了有力的工具。应用 MATLAB的编程方法和功能模块,可以搭建各种仿真系统,还可以应用丰富的时间域、频率域、相位域的仿真测量仪器。许多新一代通信系统的系统级的仿真程序出现在 MATLAB软件的演示实例中,这使得学习的效率大为提高,对技术与系统的理解已经从概念深入到电路方案和参数选取的层面。建立和谐的电磁环境,需要了解各种无线电通信系统的特性,其中包括时间域、频率域、相位域以及传输特性。特别是它们之间的定量关系可以从仿真实例运行的结果中得到。也可以说,每个仿真实例就是一个小实验平台,实验中可以对通信的相关原理,甚至通信系统进行研究,其中的分类结果也可以作为资料备查。本书应用 MATLAB仿真工具对常见的调制方式、发送滤波器、差错控制方式以及各种通信系统(包括新一代的通信系统)的相关特性进行研究。可以运行的大量仿真实例,一方面给出了仿真的结果;另一方面还给出了部分仪表测量的结果可供比对。仿真实例大部分是作者在教学与科研实践中自行编制的,小部分是 MATLAB软件中的演示仿真实例全书分以下五部分:第一部分基础知识,包括数字调制、发送滤波器、通信信号的测量与表达。第二部分常用无线电通信系统,包括公众移动通信系统、专用移动通信系统、卫星通信系统。第三部分新技术与3G,包括扩频、跳频、OFDM以及3G系统。第四部分无线接入系统与数字电视,包括蓝牙、802.1a与数字电视。第五部分天线与射频技术,包括天线阵列、射频传输线、滤波器。书中给出了简单的原理介绍、 MATLAB仿真的系统、程序(光盘)以及程序运行结果。对编程中的主要问题与注意事项作了讲解。着重讨论了各种系统及输出信号的频谱特性。对于系统的传输特性,以及影响系统传输特性的因素,譬如调制方式、差错控制方式也进行了较系统的讨论。为了保持本书简练的篇幅,没有在书中详细介绍所有仿真系统中各个模块的参数设置,也没有将雷同的系列仿真系统的程序在书中展示。购书所附的光盘提供了书中提到的全部软件。点击模块打开对话框可以仔细研究其中每个模块的参数设置。若需要还可以将其拷贝出来后按照读者的意愿,遵循 MATLAB的相关规则更改参数后进行学习研究。本书可作为高等学校通信专业的教学、科研参考书,也可供管理部门作为参考资料。本书编写中的不妥和疏漏之处,还望读者给予指出。作者的联系方式:xumil@163com,shaoyun99@sina.com徐明远邵玉斌2011年1月说明1.用 MATLAB m文件编写的程序序号在本书中是这样表示的:比如程序35表示第3章的序号为5的程序。在 MATLAB软件中用CHX35表示。2. MATLAB/Simulink程序在本书及 MATLAB软件中都用SCHX3_10表示(第3章的序号为10的程序),该仿真系统框图在本书中用图3-10表示。3.本书的程序可运行在 MATLAB2008B版本目录第一部分基础知识第1章数字调制1.1非连续相位的角度调制31.1.1FSK信号的仿真1.1.2PSK信号的仿真1412连续相位的角度调制.1.3正交幅度调制29第2章发送滤波器,·,2.1概论322.2升余弦脉冲滤波器.332.3平方根升余弦滤波器4124高斯滤波器…46第3章通信信号的测量与表达3.1通信仿真中常用的信号测量模块3.1.1 Simulink基本模块中的 Sinks子库简介,,,,,,,,,,,,,,,,,513.12 Simulink通信工具箱中的 Comm sinks子库简介….52313 Simulink信号处理工具箱中的 Signal Processing Sinks子库简介533.2信号的测量…623.21窄带随机信号的产生和波形测量3.2.2各种信号的表示和测量633.3差错控制传输特性的测量与表达72331线性码……1733.32循环码.753.33里德-索洛蒙码773.34卷积码335汉明码3.3.6BCH码3.37循环冗余码8534信号统计参数的测量.…863.4.1统计模块库86342概率密度函数873.4.3瑞利衰落信道的仿真测试……344图像的灰度直方图3.5图像和视频信号的测量与表达3.5.1模块库…3.52图像的读出与显示933.53图像加噪与滤波…,.,,,·943.54图像的二维变换与反变换963.5.5图像有损压缩“““第二部分常用无线电通信系统第4章公众移动通信系统1014.1公众通信系统概述…….1014.1.1最早期的移动通信系统014.1.2第1代移动通信系统….4.1.3第2代移动通信系统1024.1.4第2.5代移动通信系统……,,,,,,,1044.1.5第3代移动通信系统1064.1.6第4代移动通信系统…1084.2GSM全球移动通信系统.,,非1094.3CDPD蜂窝数字分组数据网……11144NADC北美数字蜂窝网1134.5PDC个人数字蜂窝电话144.6 CDMA IS-95码分多址通信系统1174.7CT-2第2代无绳电话系统184.8数字增强型无绳通信标准12049PHS个人手持式电话系统12第5章专用移动通信系统12451集群通信系统…12452APCO数字集群通信系统1255.3 TETRA欧洲数字集群通信系统12754THTS地面航空电话系统…129第6章卫星通信系统1316.1铱星系统.1316.2美国ICO卫星通信系统13363甚小孔径终端卫星通信系统…135第三部分新技术与3G第7章新技术…1417.1扩频1417.2多元扩频7.3跳频…14674正交多载波调制OFDM.150第8章3G系统15281 WCDMA码分多址通信系统15282CDMA2000码分多址通信系统157第四部分无线接入系统与数字电视第9章无线接入系统…91蓝牙系统 BLUE TOOTH16592无线局域网 HiperLAN216993无线局域网802.11标准,.非,,,,,,,,,,,,,,,,甲174第10章数字电视系统10.1数字电视广播系统…17910.2卫星广播系统182第五部分天线与射频技术第11章天线187111天线方向图18711.2均匀直线阵的波束扫描…11111187113均匀圆形阵的波束扫描189114非均匀直线阵的波达方向估计19211.1 Capon法194114.2 Music法195第12章射频…19612.1波导19612.2传输线19812.3滤波器205参考文献207
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凸优化在信号处理与通信中的应用Convex Optimization in Signal Processing and Communications
凸优化理论在信号处理以及通信系统中的应用 比较经典的通信系统凸优化入门教程ContentsList of contributorspage IxPrefaceAutomatic code generation for real- time convex optimizationJacob Mattingley and stephen Boyd1.1 Introduction1.2 Solvers and specification languages61. 3 Examples121. 4 Algorithm considerations1.5 Code generation261.6 CVXMOD: a preliminary implementation281.7 Numerical examples291. 8 Summary, conclusions, and implicationsAcknowledgments35ReferencesGradient-based algorithms with applications to signal-recoveryproblemsAmir beck and marc teboulle2.1 Introduction422.2 The general optimization model432.3 Building gradient-based schemes462. 4 Convergence results for the proximal-gradient method2.5 A fast proximal-gradient method2.6 Algorithms for l1-based regularization problems672.7 TV-based restoration problems2. 8 The source-localization problem772.9 Bibliographic notes83References85ContentsGraphical models of autoregressive processes89Jitkomut Songsiri, Joachim Dahl, and Lieven Vandenberghe3.1 Introduction893.2 Autoregressive processes923.3 Autoregressive graphical models983. 4 Numerical examples1043.5 Conclusion113Acknowledgments114References114SDP relaxation of homogeneous quadratic optimization: approximationbounds and applicationsZhi-Quan Luo and Tsung-Hui Chang4.1 Introduction1174.2 Nonconvex QCQPs and sDP relaxation1184.3 SDP relaxation for separable homogeneous QCQPs1234.4 SDP relaxation for maximization homogeneous QCQPs1374.5 SDP relaxation for fractional QCQPs1434.6 More applications of SDP relaxation1564.7 Summary and discussion161Acknowledgments162References162Probabilistic analysis of semidefinite relaxation detectors for multiple-input,multiple-output systems166Anthony Man-Cho So and Yinyu Ye5.1 Introduction1665.2 Problem formulation1695.3 Analysis of the SDr detector for the MPsK constellations1725.4 Extension to the Qam constellations1795.5 Concluding remarks182Acknowledgments182References189Semidefinite programming matrix decomposition, and radar code design192Yongwei Huang, Antonio De Maio, and Shuzhong Zhang6.1 Introduction and notation1926.2 Matrix rank-1 decomposition1946.3 Semidefinite programming2006.4 Quadratically constrained quadratic programming andts sdp relaxation201Contents6.5 Polynomially solvable QCQP problems2036.6 The radar code-design problem2086.7 Performance measures for code design2116.8 Optimal code design2146.9 Performance analysis2186.10 Conclusions223References226Convex analysis for non-negative blind source separation withapplication in imaging22Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi, and Yue Wang7.1 Introduction2297.2 Problem statement2317.3 Review of some concepts in convex analysis2367.4 Non-negative, blind source-Separation criterion via CAMNS2387.5 Systematic linear-programming method for CAMNS2457.6 Alternating volume-maximization heuristics for CAMNS2487.7 Numerical results2527.8 Summary and discussion257Acknowledgments263References263Optimization techniques in modern sampling theory266Tomer Michaeli and yonina c. eldar8.1 Introduction2668.2 Notation and mathematical preliminaries2688.3 Sampling and reconstruction setup2708.4 Optimization methods2788.5 Subspace priors2808.6 Smoothness priors2908.7 Comparison of the various scenarios3008.8 Sampling with noise3028. 9 Conclusions310Acknowledgments311References311Robust broadband adaptive beamforming using convex optimizationMichael Rubsamen, Amr El-Keyi, Alex B Gershman, and Thia Kirubarajan9.1 Introduction3159.2 Background3179.3 Robust broadband beamformers3219.4 Simulations330Contents9.5 Conclusions337Acknowledgments337References337Cooperative distributed multi-agent optimization340Angelia Nedic and asuman ozdaglar10.1 Introduction and motivation34010.2 Distributed-optimization methods using dual decomposition34310.3 Distributed-optimization methods using consensus algorithms35810.4 Extensions37210.5 Future work37810.6 Conclusions38010.7 Problems381References384Competitive optimization of cognitive radio MIMO systems via game theory387Gesualso Scutari, Daniel P Palomar, and Sergio Barbarossa11.1 Introduction and motivation38711.2 Strategic non-cooperative games: basic solution concepts and algorithms 39311.3 Opportunistic communications over unlicensed bands411.4 Opportunistic communications under individual-interferenceconstraints4151.5 Opportunistic communications under global-interference constraints43111.6 Conclusions438Ackgment439References43912Nash equilibria: the variational approach443Francisco Facchinei and Jong-Shi Pang12.1 Introduction44312.2 The Nash-equilibrium problem4412. 3 EXI45512.4 Uniqueness theory46612.5 Sensitivity analysis47212.6 Iterative algorithms47812.7 A communication game483Acknowledgments490References491Afterword494Index49ContributorsSergio BarbarossaYonina c, eldarUniversity of rome-La SapienzaTechnion-Israel Institute of TechnologyHaifaIsraelAmir beckTechnion-Israel instituteAmr El-Keyiof TechnologyAlexandra universityHaifEgyptIsraelFrancisco facchiniStephen boydUniversity of rome La sapienzaStanford UniversityRomeCaliforniaItalyUSAAlex b, gershmanTsung-Han ChanDarmstadt University of TechnologyNational Tsing Hua UniversityDarmstadtHsinchuGermanyTaiwanYongwei HuangTsung-Hui ChangHong Kong university of scienceNational Tsing Hua Universityand TechnologyHsinchuHong KongTaiwanThia KirubarajanChong-Yung chiMcMaster UniversityNational Tsing Hua UniversityHamilton ontarioHsinchuCanadaTaiwanZhi-Quan LuoJoachim dahlUniversity of minnesotaanybody Technology A/sMinneapolisDenmarkUSAList of contributorsWing-Kin MaMichael rebsamenChinese University of Hong KongDarmstadt UniversityHong KonTechnologyDarmstadtAntonio de maioGermanyUniversita degli studi di napoliFederico iiGesualdo scutariNaplesHong Kong University of Sciencealyand TechnologyHong KongJacob MattingleyAnthony Man-Cho SoStanford UniversityChinese University of Hong KongCaliforniaHong KongUSAJitkomut songsinTomer michaeliUniversity of californiaTechnion-Israel instituteLoS Angeles. CaliforniaogyUSAHaifaMarc teboulleTel-Aviv UniversityAngelia NedicTel-AvUniversity of Illinois atIsraelUrbana-ChampaignInoSLieven VandenbergheUSAUniversity of CaliforniaLos Angeles, CaliforniaUSAAsuman OzdaglarMassachusetts Institute of TechnologyYue WangBoston massachusettsVirginia Polytechnic InstituteUSAand State UniversityArlingtonDaniel p palomarUSAHong Kong University ofScience and TechnologyYinyu YeHong KongStanford UniversityCaliforniaong-Shi PangUSAUniversity of illinoisat Urbana-ChampaignShuzhong zhangIllinoisChinese university of Hong KongUSAHong KongPrefaceThe past two decades have witnessed the onset of a surge of research in optimization.This includes theoretical aspects, as well as algorithmic developments such as generalizations of interior-point methods to a rich class of convex-optimization problemsThe development of general-purpose software tools together with insight generated bythe underlying theory have substantially enlarged the set of engineering-design problemsthat can be reliably solved in an efficient manner. The engineering community has greatlybenefited from these recent advances to the point where convex optimization has nowemerged as a major signal-processing technique on the other hand, innovative applica-tions of convex optimization in signal processing combined with the need for robust andefficient methods that can operate in real time have motivated the optimization commu-nity to develop additional needed results and methods. The combined efforts in both theoptimization and signal-processing communities have led to technical breakthroughs ina wide variety of topics due to the use of convex optimization This includes solutions tonumerous problems previously considered intractable; recognizing and solving convex-optimization problems that arise in applications of interest; utilizing the theory of convexoptimization to characterize and gain insight into the optimal-solution structure and toderive performance bounds; formulating convex relaxations of difficult problems; anddeveloping general purpose or application-driven specific algorithms, including thosethat enable large-scale optimization by exploiting the problem structureThis book aims at providing the reader with a series of tutorials on a wide varietyof convex-optimization applications in signal processing and communications, writtenby worldwide leading experts, and contributing to the diffusion of these new developments within the signal-processing community. The goal is to introduce convexoptimization to a broad signal-processing community, provide insights into how convexoptimization can be used in a variety of different contexts, and showcase some notablesuccesses. The topics included are automatic code generation for real-time solvers, graphical models for autoregressive processes, gradient-based algorithms for signal-recoveryapplications, semidefinite programming(SDP)relaxation with worst-case approximationperformance, radar waveform design via SDP, blind non-negative source separation forimage processing, modern sampling theory, robust broadband beamforming techniquesdistributed multiagent optimization for networked systems, cognitive radio systems viagame theory, and the variational-inequality approach for Nash-equilibrium solutionsPrefaceThere are excellent textbooks that introduce nonlinear and convex optimization, providing the reader with all the basics on convex analysis, reformulation of optimizationproblems, algorithms, and a number of insightful engineering applications. This book istargeted at advanced graduate students, or advanced researchers that are already familiarwith the basics of convex optimization. It can be used as a textbook for an advanced graduate course emphasizing applications, or as a complement to an introductory textbookthat provides up-to-date applications in engineering. It can also be used for self-study tobecome acquainted with the state of-the-art in a wide variety of engineering topicsThis book contains 12 diverse chapters written by recognized leading experts worldwide, covering a large variety of topics. Due to the diverse nature of the book chaptersit is not possible to organize the book into thematic areas and each chapter should betreated independently of the others. a brief account of each chapter is given nextIn Chapter 1, Mattingley and Boyd elaborate on the concept of convex optimizationin real-time embedded systems and automatic code generation. As opposed to genericsolvers that work for general classes of problems, in real-time embedded optimization thesame optimization problem is solved many times, with different data, often with a hardreal-time deadline. Within this setup the authors propose an automatic code-generationsystem that can then be compiled to yield an extremely efficient custom solver for theproblem familyIn Chapter 2, Beck and Teboulle provide a unified view of gradient-based algorithmsfor possibly nonconvex and non-differentiable problems, with applications to signalrecovery. They start by rederiving the gradient method from several different perspectives and suggest a modification that overcomes the slow convergence of the algorithmThey then apply the developed framework to different image-processing problems suchas e1-based regularization, TV-based denoising, and Tv-based deblurring, as well ascommunication applications like source localizationIn Chapter 3, Songsiri, Dahl, and Vandenberghe consider graphical models for autore-gressive processes. They take a parametric approach for maximum-likelihood andmaximum-entropy estimation of autoregressive models with conditional independenceconstraints, which translates into a sparsity pattern on the inverse of the spectral-densitymatrix. These constraints turn out to be nonconvex. To treat them the authors proposea relaxation which in some cases is an exact reformulation of the original problem. Theproposed methodology allows the selection of graphical models by fitting autoregressiveprocesses to different topologies and is illustrated in different applicationsThe following three chapters deal with optimization problems closely related to SDPand relaxation techniquesIn Chapter 4, Luo and Chang consider the SDP relaxation for several classes ofquadratic-optimization problems such as separable quadratically constrained quadraticprograms(QCQPs)and fractional QCQPs, with applications in communications and signal processing. They identify cases for which the relaxation is tight as well as classes ofquadratic-optimization problems whose relaxation provides a guaranteed, finite worstcase approximation performance. Numerical simulations are carried out to assess theefficacy of the SDP-relaxation approach
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