MATLAB-SIMULINK通信系统建模与仿真实例分析
MATLAB-SIMULINK通信系统建模与仿真实例分析,Simulink在通信上应用，非常实用的一本书。內容簡介本書系統地介紹了通信建模仿真方法和模型驗證技術,並結合作者近年在教學科硏中所設計的大量基礎的和較深入的建模仿真實例,詳細討論了 Matlab/ Simulink作為仿真實現平台在電子與通信工程中應用的基木方法、技巧和難點。木書重點討論了建模仿真原理和相鬨的數值計算方法、模擬通信系統、模數轉換、調製貝編碼、信道模擬、載波與符號同步、信道均衡、跳頻系統和直接擴頻系統、通信模型正確性評估、仿寘數據驗證和數據處理技術等內容,並在仿真實例中展示了科學研究論文和報告所必須的數據處理和表現技巧本書提供了約150個建模仿真實例,80多道思考題,以及全部實例代碼和一個電子教案這些實例根據基木數學原理,結合 Simulink的S凼數編程,也使用了通信工具箱、信婋處理工具箱和相關模型庫的模塊和函數,以便於讀者追源求本,深入理解建模和仿真的實質。本書可作為高等院校通信工程、電子信息類專業的本科生和硏究生系統仿真課程的教材或進行相關課題硏究的參考書,也可作為相關專業課程設計和畢業設計等綜合性實踐教學的指導材料,還可供通信工程專業技術人員、教師等作為解決通笮系統設計、評估和建模仿真領域實際問題的參考資料。前Matlab語言由於其語法的簡潔性、代碼接近於自然數學措述方式以及具有豐富的專業凶數庫等諸多優點吸引了眾多的科學研究工作者,越來越成為科學研究、數值計算、建模仿真以及學術交流的事實標準。 Simulink作為 Matlab語言上的一個可視化建模仿真平台,起源於對自動控制系統的仿真霱求,它採用方框圖建模的形式,更加貼近於工稈習慣。目前, Matlab/ Simulink的應用已經遠遠超越了数值計算和控制系統仿真等傳統領域,在幾乎所有理工學科中形成了為數眾多的專業L具庫和函數庫,口益成為科學研究和上程設計中口常的計算和仿真試驗工具。隨著 Matlab/ Simulink通信、信號處理專業函數庫和專業工貝箱的成熟,他們日益為廣人通信技術領域的專家學者和工程師所熟悉,在通信理論硏究、算法設計、系統設計、建模仿真和性能分析驗證等方面的應用也更加鷹泛。 Simulink可化仿真工具能夠以很直觀的方框晑方式形象地對通信系統進行建模,並以「實時」和動畫的方式來將模型仿貞結果(如波形、頻譜、敷據曲線等)顯小岀來,更便於對通信系統的物理概念和運行過程的直覲理解,所以近年來在通信工程專業中得到了廣人師生的重視和廣泛應用,在理論教學、課程實踐環節以及理論和技術前沿的研究中發揮了重要作用。本書以通信原理為主線,從系統建模原理和仿真的數值計算方法入手,詳細介紹了 Matlab/ Simulink在通信系統建模和仿貞中的應用原理、內容方法和特點,並結合作者在科研和教學中的應用硏究,列舉了大量的仿真實例。通過這些實例,以期逹到兩個目的:其一是通過系統建模過程對 Matlab/ Simulink基本建模仿真方法的實質性理解,以掌握通信系統仿真的思維方法;其二是通過仿真過程和仿真結果分炘對基本通信系統原理的理解,並逐漸培養系統建模和設計的自主能力和創造力本書的特點是:(1)本書重黠討論通信工程相開專業旳系統仿真原理和應用,以通信系統媾成為主線介紹系統仿真方法,以微分方程的數偵求解和概率論為數學基礎,注重介紹通信仿真技術中基礎性的,本質性的內容,並強調仿真的數學原理和方法,而不作為一本 Matlab語言或仿真編程的介紹手間哩論的學習必須要有實踐的支持,理諍的檢驗和驗證也必須通過實踐。數理基礎在通信工程專業中的地位應當得到重視。系統仿真技術是專業理諭和系統實驗相結合的有效途徑之一,學習通信系統仿真不是學習某個系統仿真軟件的功能,而是在紮實的數理基礎和通信理論基礎上以系統仿真軟件作為工具平台的實踐活動。基於這種認織,本書沒有系統介紹 Matlab/ Simulink軟件的使用方沄和編程函數,而是把 Matlab/ Simulink視為一種方便的仿真軟件工只在通信系統建模和仿真中加以應用。因,掌握本書所介紹的系統仿真思想方法也就意味著可以使用任何計算機語言來進行通信系統的建模仿真實踐(2)本書詳細請述了 Matlab/ Simulink的建模仿真原理,把S函數作為掌握 Simulink仿真的根本,並將 Simulink可視化建模和 Matlab語言編程統一起來。並通過眾多的實例,加強了對仿手段、思想方法以及系統原理等抽像內谷的理解和應用。讓者可以運行這些實例,或改變實例中系統模塊的參敷來進行實驗,甚仝可以在這些實例的基礎上媾建更加複雜的系統模型。(3)本書在內容編排上注意由淺入深,逐本求源,由普遍方法論到實際建模實驗,中通信單元模塊的建模到綜合系統仿真,循序漸進,便於閱讀和學習。本書對通信系統建模的敭學原哩的講述比峧詳細,重視數哩基礎在通信程中的應用,注重原理的論述,授人以漁以 Matlab/ Simulink作為實驗台。特別注重講解通信系統建模和仿真理諭中根本性的和基礎性的內容。(4)鑒於通信系統仿真涉及的內容廣泛,對數學基礎要求和的通信基本理論的理解要求較髙,又特別強調矩陣數值計算方法的編程實現能力,因此在每章之未總結了主要內容並對相開的參考資料進行了綜述,以供讀者進“步深入學習相開內容時參考。本書共分八章。第一章概述了通信系統仿真的原理和方法。對仿真建模的意義、模型的類型以及仿真的數學方法進行了論述。第二章是本書的基礎,主要介紹了 Matlab/ Simulink編程和建模仿真的原理,並通過大量的實例演示了應用 Matlab/ Simulink建模仿真的方法、關鍵問題和處理技巧。希望通過這些實例和實驗實作來使讀者對 Matlab/ Simulink的建模和仿真有一個實質性的理解第三章以通信系統的基本構造為主線,對通信系統基本模塊的原理和建模方法進行了剖論,並介紹了 Matlab/ / Simulink通信τ具箱和信號處理工具箱中的常用模塊及其原理和使用方法。以這些基本模塊為元素,給岀了通信系統中從信源、調製、信道到接收解調、同步等基木單元的仿真實例第四章簡要闡述了通信系統整體構架和層次伈建模的思想要點,比較了模擬通信系統和數字通信系統的仿真框架和兩者的異冋點,並討論了描述通信系統質量和性能的主要指標第五章對模擬通信系統的建模和彷寘問題進行了詳細的討論,包括對調幅廣播波形和頻譜、傳輸、接收機自動增益控原理和性能、檢波和解調、單邊帶通佁機、調頻立體聲系統以及彩色電視信婋和系統的仿真實例。對模擬通信系統運行原理的理解能力可以视為無線電和電子工程師最基本的專業素質來衡量第六章討論了模擬信號數字化問題的原理和仿寘實例,內容包括採樣定理的原理性仿真、Δ①D轉換、非均勻量化的原理和性能仿真、pCM編解碼過程、自適應PCM以及增量調製的原理仿真和性能結果等等。第七章以數字通信系統的關鍵技術和一些較深入的問題為饼究對象,討論了以誤碼率為性能指熛的蒙特卡羅仿真建模方法,基帶數據傳輸的碼型設計與仿真,基帶帶限傳輸系統、眼圖以及信道均衡問題,數字調製的波形和頻譜仿寘問題等等。以仿真實例介紹了擴頻抗干擾系統的原理和性能分析,包括直接序列擴頻系統和跳頻系統的仿真實例第八章討諭了通信系統模型評估和仿真結果的正確性驗證等問題。鮫詳細地介紹了苳特卡羅仿真方法的實現要點,隨機數的產生,各種隨機分佈以及他們之間的關係,並討諭了以數理統計方法為主的模型和仿真數據評估方法,插值和擬合等實驗數據處理方法等。對特卡羅仿真方法的試驗精度等方面進行了性能分析。全書所有實例的模型文件和程序代碼在 Matlab(R13)版本下調試通過。另外,還提供了個電子教案。讀者需要只有微積分、概率賏統計、信號貝系統、數字信號處理和通信原理的背景知識。本書計劃學時為40學時,課堂上重點是講述通信系統仿真的概念、方法和實例應用,而在教學實踐環節中可以通過本書的眾多實例以及各章的思考題來加深對仿真方法的掌握。建議讀者在哩解仿貞原理的基礎上,對本書列舉的實例給岀自己的仿貞模型和設計參數,然後與本書的模型和程序結果進行對比,這樣比單純運行、硏究實例模型將吏能夠激發讀者的創造力,也更具趣味性和挑戰性。本書給岀的思考題一般是對實例問題的深化或拓展以及對正文的補充。許多思考題在仿真條件、系統建模上給讀者預留了很大的創造空間,解答可以靈活多樣感謝澳大利亞新南威籣士大學電了與電氣工程學院的 Jinhong Yuan教授,在我做訪問學者期間,他提供了良好的學術研究環境。在與他以及他的同事們的學術交流中得到了許多啟迪,促成了本書的完成。本書在成書過程中得到了許多專家、教授的關心和幫助,特別是在與徐眀遠教授、姚紹文教授、龍華教授、劉增力卲教授等前輩和專家的父流中深受教益。在本書的寫作和相關課程教學和輔導工作中得到了宋耀蓮、楊秋萍、朵晽老師的幫助和攴持。龍洋、吳熹等研究生也幫助完成了本書部分章節的校閱工作。清華大學岀版社的魏江冮編輯對本書的策劃、編輯和校對付出了辛苦的勞動,在此對他們表示衷心地感謝。最後要感謝我的家人,沒有他們的關心和支持,本書是不能完成的本書可作為高等院校通信工程、電子信息類專業的本科生和硏究生系統仿真課程的教材或進行相闋課題硏究的參考書,乜可作為相關專業課程設計和畢業設計等綜合性實踐教學的指導材料。現代通信系統仿真技術不僅僅是對通信理論的驗證于段,也日遆成為通信新理論硏究、新協議、新算法開發和系統總體設計的重要實驗硏究途徑,因此,本書所介紹的系統仿真思想方法對於從事通信系統設計的專業技術人員也很具有參考價值。限於筆者水平,木書定有不妥甚至錯誤之處,懇請讀者予以批評指正。作者的聯繄電子郵件地址是: shaoyun999 sina. cor。邵玉斌零零七年|二月目錄前言第1章通信系統仿真的原理和方法論1.1通信系統仿真的現實意義·11.2計算機仿真的過程1.2.1系統仿真的數學基礎1.2.2計算機仿真的一般過程1.3通信系統模型的分類1.3.1按照系統層次分類1.3.2按照信號類型分類1.33按照系統特徵分類1.4通信系統仿真的方法14.1基於動態系統模型的狀態方程求解方法∵·14.2基於概率模型的蒙特卡羅方法∴1.43混合方法171.5通信系統仿真的優點和局限性1.6系統建模仿真方法與仿真工具···16.1系統建模仿真方法與仿真工具的關係1.6.2仿真環境的構成和要求1.6.3常用仿真工貝的選擇1.7小結舆文獻綜述1.8思考題第2章 Matlab/ Simulink系統建模和仿真基礎」2.1 Matlab編程仿真的方法21.1概述2.1.2靜態系統的 Matlab編程仿真2.1.3連續動態系統的 Matlab編程仿真2.1.4離散動態系統的 Matlab編程仿真2.1.5基於數據流和基於時間流的仿真方法2.2 Simulink仿真基礎2.1系統模型的方程和圖形化描述222 Simulink仿真平台2.2.3構建一個簡覃的 Simulink仿真系統224 Simulink子系統構建、封裝和自定義模塊庫23 Simulink的工作原理—S函數23.1S函數的工作原理2.32用 Matlab語言編寫S函數2.4用S函數編寫 Simulink基本模塊·2.4.1信源模塊·.··2.4.2信宿和信號顯示模塊952.4.3信號傳輸模塊2.5 Simulink仿真的數據結構和編程調用方法1082.51 Simulink中數據流的向量和矩陣形式2.52 Simulink中數據結構的轉換·253 Simulink與 Matlab的交互·1212.5.4編程調用仿真模型1242.6 Simulink在電子與通信系統仿真中的幾個關鍵問題12626.1系統仿真速率的設計和選擇12626,2並/串轉換和混合速率系統仿真2.6.3不同層次的仿真模型1302.6.4用 Simulink求解方程·……2.6.5同一數學模型的多種計算機仿真實现方法1372.7聲卡在 Simulink仿真模型中的應用·13727.1 Matlab興聲卜的接口函數∵1382.7.2 Simulink與聲卡的接口模塊139273在 Simulink中組建虚擬儀器2.8小結與文獻綜述1452.9思考題146第3章基本通信模塊的建模與分析1493.1濾波器模型1493.1.1濾波器的類型、參數指熛與設計1493.L.2瀘波器的實現1593.2信源模型1623.2.1確定信源1623.2.2偽隨機碼源1633.2.3統計信源一噪聲源1643.3信號參數的測量和分析3.3.1信號的能量和功率1653.3.2信號直流份量和交流份量1653.3.3離散時間信號的統計參數1663.3.4信號的頻域參數1693.4信道模型1903.4.1加性高斯白噪聲信道1903.42帶限加性噪聲信道·19134.3離散時間信道指標的定量計算1923.4.4錯誤概率信道∵1943.5調製舆解調3.5.1調製的通帶和基帶模型1973.5.2模擬調製與解調模型3.5.3數字調製與解調模型2043.6鎖相環和載波提取20636.1鎖相環的構成和建模仿真·2063.6.2用於或波提取的鎖相環仿真3.6.3鎖相頻率合成器的仿真3.7小結舆文獻綜述··2193.8思考題第4章構建通信系統仿真模型2214.1通信系統的基本模型4.1.1模擬通信系統基本模型·4.1.2數字通信系統基本模型234.2通信系統主要性能指標2244.3通信系統建模的要點4.4小結和文獻綜述·2:304.5思考題·第5章模擬通信系統的建模仿真5.1詞幅廣播系統的仿真5.2調幅的包絡檢波和相干解調性能仿頁比較5.3頻分復用和超外差接收機的仿真模型·365.4自動增益控制(AGC)原理與仿真2385.5調頻立體聲廣播系統的建模仿真55.1調頻立體聲廣播的信號結構和仿真模型··5.5.2調頻立體聲接收機模型5.6單邊帶調幅系統的建模仿真·445.6.1希爾伯特變換445.6.2單邊帶調嗝與解調原理56.3一個簡化的單邊帶電台仿真57彩色電視系統的建模仿真2535.7.1電視掃瞄原理的仿真5.7.2彩色電視信號的構成和頻譜仿真5.7.3簡化的彩色電視接收機仿真5.8小結與文獻綜述59思考题第6章模擬信號數字化6.1採樣定理的原理仿頁62A/D和D/A轉換器的仿真2676.3PCM編碼和解碼6.3.1信號的壓縮和擴張2686.3:2PCM編碼和解碼2716.4DPM編碼與解碼2766.5增量讖製2796.6小結與文獻綜述6.7思考题.第章數字通信系統的建模仿真857.1進制傳輸的錯誤率仿真·7.2基帶傳輸碼型設計··2877.2.1二電平碼2887.22三電平碼·7.3帶限基帶傳輸系統的仿真2947.3.1眼圖和無碼間串擾波形·7.32基帶傳輸系統的仿真2977.3.3定時提取系統的仿真7.3.4信道的時域均衡·3007.4數字調製的仿真3057.4.1信號的向量表小∵·3064.2數字調製信號的向量表示和仿真3077.5擴頻系統的仿真5.1偽隨機碼的產生7.5.2直接序列擴頻系統53跳擴類系統··317.6小結興文獻綜述3347.7思考题第8章通信系統建模仿真的評估3378.1概运8.2概率模型和蒙特卡羅方法∵3398.3隨機數的產生和常用隨機分佈8.3.1均勻分佈隨機數的產生3108.32產生其他常用隨機分佈的方法833產生任意指定區間上的均勻分佈3438.3.4三角分佈
- 2020-12-10下载
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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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