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William C. Jakes, Microwave Mobile Communications part2/4-iteye
来自 : www.iteye.com/resource/yangyan 发布时间:2021-03-26
\" utm=\"distribute.pc_relevant_download.none-task-\">\"\"71.87MB

William C. Jakes, Microwave Mobile Communications .pdf

2017-12-08

William C. Jakes, Microwave Mobile Communications,通信类的经典之作,本资源是pdf文档,共645页

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Microwave Mobile Communications 电子书

2010-04-19

好不容易才找到的Microwave Mobile Communications 电子书。这本书的作者Jakes第一次提出了衰落信道的jakes模型。通信人不可错过的经典。

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SQL Server 2008实战

2013-07-06

SQL Server 2008 实战,易懂,很难得的一本书

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Jakes 模型实现代码

2012-12-16

Jakes模型的具体实现过程,对于理解Jakes模型会有辅助作用,最好是先看文献了解后在看代码

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Jakes模型的matlab程序仿真.zip

2020-05-06

jakes信道模型的仿真matlab程序仿真,包含注释,原理讲解等等内容,需要的尽管下载,如能帮助到您,十分开心

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Jakes model

2015-05-04

Jakes model的matlab代码

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jakes信道模型

2011-10-10

一个JAKES仿真模型的matlab代码

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基于Jakes瑞利信道衰落仿真.m

2020-06-14

利用MATLAB,编写了基于基于Jakes瑞利信道衰落,给出了瑞丽衰落的理论值和仿真值之间的对比以及功率谱密度函数

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论文研究-改进Jakes信道模拟器的建模与仿真实现 .pdf

2019-08-21

改进Jakes信道模拟器的建模与仿真实现,夏东君,,瑞利衰落信道的仿真模型是许多信道仿真模型的基础。无线信道作为通信信道的重要组成部分,它的信道仿真经常要求产生满足下列条件

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Clarke_Jakes_Zheng模型matlab仿真.zip

2020-06-19

关于无线信道衰落的MATLAB仿真,有几种模型,Jake、 Zheng、还有瑞利衰落信道,有需要的可以下载看看,自己用过的 可以运行,有什么问题可以提出来

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验证jakes模型幅度服从瑞利分布

2017-03-16

在已经仿真jakes模型公式的基础之上,验证该仿真公式输出信号的幅度统计概率服从瑞利分布,相位服从均匀分布

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多径衰落一径的Wss-jakes模型仿真

2016-11-14

多径衰落仿真

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Principles of Communication System Simulation with Wireless Aplications (英文版PDF)

2009-12-31

Principles of Communication System Simulation with Wireless Aplications【作者】: William H. Tranter K. Sam Shanmugan Theodore S. Rappaport Kurt L. Kosbar【页数 】:800【出版社】 :PRENTICE HALL【出版日期】:2003【文件格式】:pdfCONTENTSPREFACE xviiPart I Introduction 11 THEROLEOF SIMULATION 11.1 Examples of Complexity 21.1.1 The Analytically Tractable System 31.1.2 The Analytically Tedious System 51.1.3 The Analytically Intractable System 71.2 Multidisciplinary Aspects of Simulation 81.3 Models 111.4 Deterministic and Stochastic Simulations 141.4.1 An Example of a Deterministic Simulation 161.4.2 An Example of a Stochastic Simulation 171.5 The Role of Simulation 191.5.1 Link Budget and System-Level Specification Process 201.5.2 Implementation and Testing of Key Components 221.5.3 Completion of the Hardware Prototype and Validationof the Simulation Model 221.5.4 End-of-Life Predictions 221.6 Software Packages for Simulation 231.7 A Word of Warning 261.8 The Use of MATLAB 271.9 Outline of the Book 271.10 Further Reading 28 2 SIMULATION METHODOLOGY 312.1 Introduction 322.2 Aspects of Methodology 342.2.1 Mapping a Problem into a Simulation Model 342.2.2 Modeling of Individual Blocks 412.2.3 Random Process Modeling and Simulation 472.3 Performance Estimation 492.4 Summary 522.5 Further Reading 522.6 Problems 52Part II Fundamental Concepts and Techniques 553 SAMPLINGANDQUANTIZING 553.1 Sampling 563.1.1 The Lowpass Sampling Theorem 563.1.2 Sampling Lowpass Random Signals 613.1.3 Bandpass Sampling 613.2 Quantizing 653.3 Reconstruction and Interpolation 713.3.1 Ideal Reconstruction 713.3.2 Upsampling and Downsampling 723.4 The Simulation Sampling Frequency 783.4.1 General Development 793.4.2 Independent Data Symbols 813.4.3 Simulation Sampling Frequency 833.5 Summary 873.6 Further Reading 893.7 References 903.8 Problems 904 LOWPASSSIMULATION MODELS FOR BANDPASSSIGNALS AND SYSTEMS 954.1 The Lowpass Complex Envelope for Bandpass Signals 954.1.1 The Complex Envelope: The Time-Domain View 964.1.2 The Complex Envelope: The Frequency-Domain View 1084.1.3 Derivation of Xd(f) and Xq(f) from X (f) 1104.1.4 Energy and Power 1114.1.5 Quadrature Models for Random Bandpass Signals 1124.1.6 Signal-to-Noise Ratios 1154.2 Linear Bandpass Systems 1184.2.1 Linear Time-Invariant Systems 1184.2.2 Derivation of hd(t) and hq(t) from H(f) 1224.3 Multicarrier Signals 1254.4 Nonlinear and Time-Varying Systems 1284.4.1 Nonlinear Systems 1284.4.2 Time-Varying Systems 1304.5 Summary 1324.6 Further Reading 1334.7 References 1344.8 Problems 1344.9 Appendix A: MATLAB Program QAMDEMO 1394.9.1 Main Program: c4 qamdemo.m 1394.9.2 Supporting Routines 1404.10 Appendix B: Proof of Input-Output Relationship 1415 FILTERMODELSAND SIMULATION TECHNIQUES 1435.1 Introduction 1445.2 IIR and FIR Filters 1465.2.1 IIR Filters 1465.2.2 FIR Filters 1475.2.3 Synthesis and Simulation 1475.3 IIR and FIR Filter Implementations 1485.3.1 Direct Form II and Transposed DirectForm II Implementations 1485.3.2 FIR Filter Implementation 1545.4 IIR Filters: Synthesis Techniques and Filter Characteristics 1555.4.1 Impulse-Invariant Filters 1555.4.2 Step-Invariant Filters 1565.4.3 Bilinear z-Transform Filters 1575.4.4 Computer-Aided Design of IIR Digital Filters 1655.4.5 Error Sources in IIR Filters 1675.5 FIR Filters: Synthesis Techniques and Filter Characteristics 1675.5.1 Design from the Amplitude Response 1705.5.2 Design from the Impulse Response 1775.5.3 Implementation of FIR Filter Simulation Models 1805.5.4 Computer-Aided Design of FIR Digital Filters 1845.5.5 Comments on FIR Design 1865.6 Summary 1865.7 Further Reading 1895.8 References 1895.9 Problems 1905.10 Appendix A: Raised Cosine Pulse Example 1925.10.1 Main program c5 rcosdemo.m 1925.10.2 Function file c5 rcos.m 1925.11 Appendix B: Square Root Raised Cosine Pulse Example 1935.11.1 Main Program c5 sqrcdemo.m 1935.11.2 Function file c5 sqrc.m 1935.12 Appendix C: MATLAB Code and Data for Example 5.11 1945.12.1 c5 FIRFilterExample.m 1955.12.2 FIR Filter AMP Delay.m 1965.12.3 shift ifft.m 1985.12.4 log psd.m 1986 CASESTUDYHASE-LOCKED LOOPSAND DIFFERENTIAL EQUATION METHODS 2016.1 Basic Phase-Locked Loop Concepts 2026.1.1 PLL Models 2046.1.2 The Nonlinear Phase Model 2066.1.3 Nonlinear Model with Complex Input 2086.1.4 The Linear Model and the Loop Transfer Function 2086.2 First-Order and Second-Order Loops 2106.2.1 The First-Order PLL 2106.2.2 The Second-Order PLL 2146.3 Case Study: Simulating the PLL 2156.3.1 The Simulation Architecture 2156.3.2 The Simulation 2166.3.3 Simulation Results 2196.3.4 Error Sources in the Simulation 2206.4 Solving Differential Equations Using Simulation 2236.4.1 Simulation Diagrams 2246.4.2 The PLL Revisited 2256.5 Summary 2306.6 Further Reading 2316.7 References 2316.8 Problems 2326.9 Appendix A: PLL Simulation Program 2366.10 Appendix B: Preprocessor for PLL Example Simulation 2376.11 Appendix C: PLL Postprocessor 2386.11.1 Main Program 2386.11.2 Called Routines 2396.12 Appendix D: MATLAB Code for Example 6.3 2417 GENERATING AND PROCESSING RANDOM SIGNALS 2437.1 Stationary and Ergodic Processes 2447.2 Uniform Random Number Generators 2487.2.1 Linear Congruence 2487.2.2 Testing Random Number Generators 2527.2.3 Minimum Standards 2567.2.4 MATLAB Implementation 2577.2.5 Seed Numbers and Vectors 2587.3 Mapping Uniform RVs to an Arbitrary pdf 2587.3.1 The Inverse Transform Method 2597.3.2 The Histogram Method 2647.3.3 Rejection Methods 2667.4 Generating Uncorrelated Gaussian Random Numbers 2697.4.1 The Sum of Uniforms Method 2707.4.2 Mapping a Rayleigh RV to a Gaussian RV 2737.4.3 The Polar Method 2757.4.4 MATLAB Implementation 2767.5 Generating Correlated Gaussian Random Numbers 2777.5.1 Establishing a Given Correlation Coefficient 2777.5.2 Establishing an Arbitrary PSDor Autocorrelation Function 2787.6 Establishing a pdf and a PSD 2827.7 PN Sequence Generators 2837.8 Signal Processing 2907.8.1 Input/Output Means 2917.8.2 Input/Output Cross-Correlation 2917.8.3 Output Autocorrelation Function 2927.8.4 Input/Output Variances 2937.9 Summary 2937.10 Further Reading 2947.11 References 2947.12 Problems 2957.13 Appendix A: MATLAB Code for Example 7.11 2997.14 Main Program: c7 Jakes.m 2997.14.1 Supporting Routines 3008 POSTPROCESSING 3038.1 Basic Graphical Techniques 3048.1.1 A System Example /4 DQPSK Transmission 3048.1.2 Waveforms, Eye Diagrams, and Scatter Plots 3078.2 Estimation 3098.2.1 Histograms 3098.2.2 Power Spectral Density Estimation 3168.2.3 Gain, Delay, and Signal-to-Noise Ratios 3238.3 Coding 3298.3.1 Analytic Approach to Block Coding 3308.3.2 Analytic Approach to Convolutional Coding 3338.4 Summary 3368.5 Further Reading 3368.6 References 3388.7 Problems 3398.8 Appendix A: MATLAB Code for Example 8.1 3428.8.1 Main Program: c8 pi4demo.m 3428.8.2 Supporting Routines 3449 INTRODUCTION TO MONTE CARLO METHODS 3479.1 Fundamental Concepts 3479.1.1 Relative Frequency 3489.1.2 Unbiased and Consistent Estimators 3499.1.3 Monte Carlo Estimation 3499.1.4 The Estimation of 3519.2 Application to Communications Systems The AWGN Channel 3549.2.1 The Binomial Distribution 3559.2.2 Two Simple Monte Carlo Simulations 3599.3 Monte Carlo Integration 3669.3.1 Basic Concepts 3689.3.2 Convergence 3709.3.3 Confidence Intervals 3719.4 Summary 3759.5 Further Reading 3759.6 References 3759.7 Problems 37610 MONTE CARLO SIMULATIONOF COMMUNICATION SYSTEMS 37910.1 Two Monte Carlo Examples 38010.2 Semianalytic Techniques 39310.2.1 Basic Considerations 39410.2.2 Equivalent Noise Sources 39710.2.3 Semianalytic BER Estimation for PSK 39810.2.4 Semianalytic BER Estimation for QPSK 40010.2.5 Choice of Data Sequence 40410.3 Summary 40510.4 References 40610.5 Problems 40610.6 Appendix A: Simulation Code for Example 10.1 40810.6.1 Main Program 40810.6.2 Supporting Program: random binary.m 40910.7 Appendix B: Simulation Code for Example 10.2 41010.7.1 Main Program 41010.7.2 Supporting Programs 41410.7.3 vxcorr.m 41410.8 Appendix C: Simulation Code for Example 10.3 41510.8.1 Main Program: c10 PSKSA.m 41510.8.2 Supporting Programs 41610.9 Appendix D: Simulation Code for Example 10.4 41810.9.1 Supporting Programs 41911 METHODOLOGY FOR SIMULATINGA WIRELESS SYSTEM 42111.1 System-Level Simplifications and Sampling Rate Considerations 42311.2 Overall Methodology 42411.2.1 Methodology for Simulation of the Analog Portionof the System 42911.2.2 Summary of Methodology for Simulatingthe Analog Portion of the System 44111.2.3 Estimation of the Coded BER 44111.2.4 Estimation of Voice-Quality Metric 44111.2.5 Summary of Overall Methodology 44211.3 Summary 44311.4 Further Reading 44311.5 References 44411.6 Problems 444Part III Advanced Models and Simulation Techniques 44712 MODELING AND SIMULATION OF NONLINEARITIES 44712.1 Introduction 44812.1.1 Types of Nonlinearities and Models 44812.1.2 Simulation of Nonlinearities Factors to Consider 44912.2 Modeling and Simulation of Memoryless Nonlinearities 45112.2.1 Baseband Nonlinearities 45212.2.2 Bandpass Nonlinearities Zonal Bandpass Model 45312.2.3 Lowpass Complex Envelope(AM-to-AM and AM-to-PM) Models 45512.2.4 Simulation of Complex Envelope Models 46112.2.5 The Multicarrier Case 46212.3 Modeling and Simulation of Nonlinearities with Memory 46812.3.1 Empirical Models Based on Swept Tone Measurements 47012.3.2 Other Models 47212.4 Techniques for Solving Nonlinear Differential Equations 47512.4.1 State Vector Form of the NLDE 47612.4.2 Recursive Solutions of NLDE-Scalar Case 47912.4.3 General Form of Multistep Methods 48312.4.4 Accuracy and Stability of Numerical Integration Methods 48312.4.5 Solution of Higher-Order NLDE-Vector Case 48512.5 PLL Example 48612.5.1 Integration Methods 48612.6 Summary 48812.7 Further Reading 48812.8 References 48912.9 Problems 49012.10 Appendix A: Saleh s Model 49312.11 Appendix B: MATLAB Code for Example 12.2 49412.11.1 Supporting Routines 49513 MODELING AND SIMULATIONOF TIME-VARYING SYSTEMS 49713.1 Introduction 49713.1.1 Examples of Time-Varying Systems 49813.1.2 Modeling and Simulation Approach 49913.2 Models for LTV Systems 50013.2.1 Time-Domain Description for LTV System 50013.2.2 Frequency Domain Description of LTV Systems 50313.2.3 Properties of LTV Systems 50513.3 Random Process Models 51113.4 Simulation Models for LTV Systems 51513.4.1 Tapped Delay Line Model 51513.5 MATLAB Examples 51813.5.1 MATLAB Example 1 51813.5.2 MATLAB Example 2 52013.6 Summary 52213.7 Further Reading 52313.8 References 52313.9 Problems 52313.10 Appendix A: Code for MATLAB Example 1 52513.10.1 Supporting Program 52613.11 Appendix B: Code for MATLAB Example 2 52713.11.1 Supporting Routines 52813.11.2 mpsk pulses.m 52814 MODELING AND SIMULATIONOF WAVEFORM CHANNELS 52914.1 Introduction 52914.1.1 Models of Communication Channels 53014.1.2 Simulation of Communication Channels 53114.1.3 Discrete Channel Models 53214.1.4 Methodology for Simulating CommunicationSystem Performance 53214.1.5 Outline of Chapter 53314.2 Wired and Guided Wave Channels 53314.3 Radio Channels 53414.3.1 Tropospheric Channel 53614.3.2 Rain Effects on Radio Channels 53714.4 Multipath Fading Channels 53814.4.1 Introduction 53814.4.2 Example of a Multipath Fading Channel 53814.4.3 Discrete Versus Diffused Multipath 54514.5 Modeling Multipath Fading Channels 54614.6 Random Process Models 54714.6.1 Models for Temporal Variations14.6.2 Important Parameters 55014.7 Simulation Methodology 55214.7.1 Simulation of Diffused Multipath Fading Channels 55314.7.2 Simulation of Discrete Multipath Fading Channels 55814.7.3 Examples of Discrete Multipath Fading Channel Models 56514.7.4 Models for Indoor Wireless Channels 57114.8 Summary 57114.9 Further Reading 57214.10 References 57214.11 Problems 57514.12 Appendix A: MATLAB Code for Example 14.1 57714.12.1 Main Program 57714.12.2 Supporting Functions 57814.13 Appendix B: MATLAB Code for Example 14.2 58014.13.1 Main Program 58014.13.2 Supporting Functions 58115 DISCRETE CHANNEL MODELS 58315.1 Introduction 58415.2 Discrete Memoryless Channel Models 58615.3 Markov Models for Discrete Channels with Memory 58915.3.1 Two-State Model 58915.3.2 N-state Markov Model 59615.3.3 First-Order Markov Process 59715.3.4 Stationarity 59715.3.5 Simulation of the Markov Model 59815.4 Example HMMs Gilbert and Fritchman Models 60115.5 Estimation of Markov Model Parameters 60415.5.1 Scaling 61115.5.2 Convergence and Stopping Criteria 61215.5.3 Block Equivalent Markov Models 61315.6 Two Examples 61515.7 Summary 62115.8 Further Reading 62215.9 References 62215.10 Problems 62315.11 Appendix A: Error Vector Generation 62715.11.1 Program: c15 errvector.m 62715.11.2 Program: c15 hmmtest.m 62815.12 Appendix B: The Baum-Welch Algorithm 62915.13 Appendix C: The Semi-Hidden Markov Model 63215.14 Appendix D: Run-Length Code Generation 63615.15 Appendix E: Determination of Error-Free Distribution 63715.15.1 c15 intervals1.m 63715.15.2 c15 intervals2.m 63716 EFFICIENT SIMULATION TECHNIQUES 63916.1 Tail Extrapolation 64016.2 pdf Estimators 64216.3 Importance Sampling 64516.3.1 Area of an Ellipse 64616.3.2 Sensitivity to the pdf 65516.3.3 A Final Twist 65616.3.4 The Communication Problem 65716.3.5 Conventional and Improved Importance Sampling 65916.4 Summary 66016.5 Further Reading 66016.6 References 66216.7 Problems 66216.8 Appendix A: MATLAB Code for Example 16.3 66516.8.1 Supporting Routines 66917 CASE STUDY: SIMULATIONOF A CELLULAR RADIO SYSTEM 67117.1 Introduction 67117.2 Cellular Radio System 67317.2.1 System-Level Description 67317.2.2 Modeling a Cellular Communication System 67617.3 Simulation Methodology 68817.3.1 The Simulation 68817.3.2 Processing the Simulation Results 70017.4 Summary 70617.5 Further Reading 70617.6 References 70717.7 Problems 70817.8 Appendix A: Program for Generating the Erlang B Chart 71017.9 Appendix B: Initialization Code for Simulation 71217.10 Appendix C: Modeling Co-Channel Interference 71417.10.1 Wilkinson s Method 71517.10.2 Schwartz and Yeh s Method 71717.11 Appendix D: MATLAB Code for Wilkinson s Method 71818 TWO EXAMPLE SIMULATIONS 71918.1 A Code-Division Multiple Access System 72018.1.1 The System 72018.1.2 The Simulation Program 72418.1.3 Example Simulations 72618.1.4 Development of Markov Models 72918.2 FDM System with a Nonlinear Satellite Transponder 73418.2.1 System Description and Simulation Objectives 73418.2.2 The Overall Simulation Model 73718.2.3 Uplink FDM Signal Generation 73818.2.4 Satellite Transponder Model 74018.2.5 Receiver Model and Semianalytic BER Estimator 74118.2.6 Simulation Results 74218.2.7 Summary and Conclusions 74418.3 References 74618.4 Appendix A: MATLAB Code for CDMA Example 74718.4.1 Supporting Functions 75018.5 Appendix B: Preprocessors for CDMA Application 75318.5.1 Validation Run 75318.5.2 Study Illustrating the Effect of the Ricean K-Factor 75318.6 Appendix C: MATLAB Function c18 errvector.m 75518.7 Appendix D: MATLAB Code for Satellite FDM Example 75618.7.1 Supporting Functions 760

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MIMO-OFDM Wireless Communications with MATLAB

2018-11-13

ContentsPreface xiiiLimits of Liability and Disclaimer of Warranty of Software xv1 The Wireless Channel: Propagation and Fading 11.1 Large-Scale Fading 41.1.1 General Path Loss Model 41.1.2 Okumura/Hata Model 81.1.3 IEEE 802.16d Model 101.2 Small-Scale Fading 151.2.1 Parameters for Small-Sc

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经典滤波器的MATLAB仿真源程序.doc

2020-06-21

1%巴特沃斯低通模拟圆形滤波器 clear all; n=0:0.01:2; for i=1:4 switch i case 1 N=2; case 2 N=5; case 3 N=10; case 4 N=20; end [z,p,k]=buttap(N; %函数buttap--设计巴特沃斯低通滤波器 [b,a]=zp2tf(z,p,k; %函数zp2tf--零极点增益模型转换为传递函数模型 [

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基于MATLAB的无线信道仿真及研究

2018-10-10

本设计课题任务的内容为:对OFDM系统无线信道进行研究,利用仿真器进行仿真,研究分析电磁波在该无线信道中的传播和变化规律。具体要求:(1)在研究无线信道传播理论基础上,分析无线信道传播特性,建立各种衰落信道的结构模型,设计无线信道抽头延迟线模型和Jakes仿真模型。 (2)对路径损耗信道模型进行分析,比较各模型的特点,仿真分析模型误差,提出各种模型的适用环境。 (3)利用Jakes仿真器,对小尺度衰落信道进行计算机仿真,验证平坦衰落和频率选择性衰落信道特性,分析小尺度衰落的各种性能参数。 (4)对OFDM系统进行仿真,通过比较加保护间隔和不加保护间隔系统的误码率,给出OFDM具有独特的抗多径衰落特性。 (5)通过分析移动台移动速度和周围环境对系统误码率、信号包络、多普勒功率谱和传递函数等系统参数的影响,给出小尺度衰落随移动台移动速度和周围环境的变化关系。

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SISO信道模型(室外).zip

2020-01-02

MATLAB实现SISO信道模型中的室外模型,包括FEGN-Clarke/Gans信道模型,改进的频域/时域FEGN信道模型,jakes模型,基于射线的模型,SUI信道模型

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基于遗传算法的IIR滤波器衰落信道模拟器的设计

2020-06-04

本文设计了一种使用IIR滤波器(称为多普勒滤波器)的瑞利衰落信道模拟器,该滤波器用于近似Jakes多普勒频谱。 我们主要关注多普勒滤波器的设计并将其建模为优化问题。 本文证明了该问题的非凸性,并使用遗传算法对其进行了优化,这在该领域以前从未使用过。 仿真结果表明,GA收敛于与Jakes PSD的非常精确近似相对应的解。 最后,验证了模拟器的几个统计特性,包括相关函数和交叉穿越率(LCR),所有这些特性都与理论预测非常吻合。

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发布于 : 2021-03-26 阅读(0)
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