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Supervised Machine Learning Approach based Adaptive Modulation Order Selection in Spatial Modulation

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目录

声明

Table of Contents

ABSTRACT

LIST OF ACRONYMS

1.Introduction

1.1 Research Background and Motivation

1.2 Overseas and Domestic Research Status

1.3 Main Research Content

1.4 Thesis Organization

2.Fundamentals of Adaptive MOS in SM and ML

2.1.2 Adaptive Modulation

2.1.3 System Model of Adaptive MOS in SM

2.1.4 Simulation Results

2.2 Overview of Machine Learning

2.2.2 Supervised ML Algorithms

2.2.3 Restraints and Drawbacks

2.3 Chapter Summary

3.Design and Analysis of the KNN and SVM based Adaptive MOS in SM

3.1 K-Nearest Neighbors

3.1.1 Working Principle of KNN

3.1.2 Distance Metrics of KNN

3.2.1 Linear Sephratio with Hyperplanes

3.2.2 Lagrangian Duality of SVM Optimization Problem

3.2.3 Soft Margin Classification of SVM

3.2.4 Nonlinear SVMs:Kernels

3.3 System Model

3.4 Problem Analysis and Data Training

3.4.1 Problem Conversion

3.4.2 Training Data Preparation

3.5 Proposed Supervised MLCs based Adaptive MOS Schemes

3.6 Complexity Analysis

3.7 Simulation Resuns

3.8 Chapter Summary

4.Design and Analysis of the ANN based adaptive MOS in SM

4.1.1 Introduction

4.1.2 Multilayer Perceptron

4.1.3 Feed-Forward Networks

4.1.4 Backpropagation with Gradient Descent

4.2 System Model

4.3.1 Problem Analysis and Data Training

4.3.2 ANN-Adaptive MOS

4.3.3 Complexity Analysis

4.4 Simulation Results

4.5 Chapter Summary

5.Conclusions and Prospects

5.1 Thesis Conclusions

5.2 Future Research Prospects

References

Acknowledgement

Research Achievements

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著录项

  • 作者

    TWARAYISENZE JEAN PAUL;

  • 作者单位

    山东大学;

  • 授予单位 山东大学;
  • 学科 信息与通信工程
  • 授予学位 硕士
  • 导师姓名 白智全;
  • 年度 2021
  • 页码
  • 总页数
  • 原文格式 PDF
  • 正文语种 chi
  • 中图分类 O4TN9;
  • 关键词

  • 入库时间 2022-08-17 11:23:27

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