基於卷積神經網路之調變分類技術研究

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姓名

黃柏源(Bo-Yuan Huang) 查詢紙本館藏

畢業系所

通訊工程學系在職專班

論文名稱

基於卷積神經網路之調變分類技術研究
(Modulation Classification Using Convolutional Neural Networks)

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摘要(中)

綜觀軍事通訊發展，在軍事電子戰應用上，針對戰場上的頻譜監控與訊號情報蒐集，如何在高複雜電磁環境下截獲敵方未知通聯訊號，快速的完成偵測、辨識、解譯，以即時獲取敵方情資，在電子戰中至關重要，其中訊號調變類型自動分類，為軍事通訊截收中的關鍵技術。
　　傳統基於特徵擷取(feature extraction)的訊號調變分類的方法，需要事先分析出各種特徵參數，再利用決策樹(decision tree)或各種機器學習(machine learning)的方式，從擷取到的特徵資料中訓練出有效的分類模型，此種方式仰賴人為擷取的特徵能夠確實提供訊號分類所需的完整資訊，然而面對通道的各種變化，人工分析得到的特徵值(expert feature)往往會受到干擾而造成分類效果不佳。
　　本文提出基於卷積網路(Convolutional Neural Network)之調變分類技術，神經網路可從訓練資料(training data)中自我學習(learning from data)，自動擷取特徵並分類，實驗結果顯示，深度卷積神經網路的分類方式有更好的抗干擾性，我們綜合了各個測試的成果，提出的模型在SNR為0dB~20dB 的範圍內，調變分類預測的準確度達到94.05%的不錯表現。

摘要(英)

While investigating the development of military communication in the application of military electronic warfare, how to detect, identify and decode signals of interesting in the high-complex electromagnetic environment is extremely important. Automatic modulation recognition and classification has become a necessary technology in military electronic warfare.

Based on feature extraction, traditional modulation classification require prior analysis of various feature parameters, and then use decision trees or machine learning methods to extract features. The classification model is trained based on the captured features. This method relies on the expert features
providing sufficient information for signal classification. However, in the face of varied communication channel, the artificial expert features often be interfered and causes poor classification results.

This paper proposes a modulation classification technique based on Convolutional Neural Network. The neural network can learn from training data, extract features and classify signals automatically. The experimental results show that modulation classification using convolutional neural network provide better anti-interference performance. Analyses show that the proposed model yields an average classification accuracy of 94.05% at varying SNR conditions ranging from 0dB to 20dB.

關鍵字(中)

★ 調變分類
★ 深度學習
★ 卷積神經網路
★ 訊號處理

關鍵字(英)

★ Modulation Classification
★ Deep Learning
★ Convolutional Neural Networks
★ Signal Processing

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 VIII
第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 論文架構 3
第二章深度學習介紹 4
2.1 深度學習簡介 4
2.1.1 感知器 4
2.1.2 多層感知器 5
2.1.3 激活函數 6
2.1.4 損失函數 9
2.1.5 優化函數 10
2.1.6 訓練神經網路 11
2.2 卷積神經網路 13
2.2.1 卷積層 14
2.2.2 池化層 15
2.2.3 全連接層 16
2.3 循環神經網路 16
2.3.1 循環神經網路介紹 17
2.3.2 長短期記憶網路 20
第三章深度學習應用於調變分類技術相關文獻 24
3.1 訊號資料集 24
3.2 卷積神經網路在調變分類上的應用 25
3.3 改良版卷積神經網路在調變分類上的應用 27
3.4 長短期記憶模型在調變分類上的應用 28
3.5 小結 30
第四章提出之卷積網路調變分類模型 31
4.1 測試及訓練資料集 31
4.1.1 RadioML 2016.10 alpha 資料集的限制 31
4.1.2 修改版 RadioML 資料集 31
4.2 不同 CNN 模型比較 32
4.2.1 深度卷積神經網路 32
4.2.2 測試相關資訊 33
4.2.3 測試結果 34
4.3 資料型態比較 35
4.3.1 資料型態簡介 36
4.3.2 測試相關資訊 37
4.3.3 測試結果 38
4.4 不同 SNR 分佈的訓練資料比較 40
4.4.1 高雜訊訊訊號資料觀察 40
4.4.2 測試相關資訊 43
4.4.3 測試結果 43
4.5 不同資料長度比較 45
4.5.1 混淆矩陣 45
4.5.2 測試相關資訊 46
4.5.3 測試結果 47
4.6 提出之訊號分類模型 48
第五章實驗結果與分析討論 51
5.1 訓練模型 51
5.2 混淆矩陣分析 53
5.3 應用於各種長度資料 54
第六章結論與未來展望 57
參考文獻 59

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指導教授

林嘉慶(Jia-Chin Lin)

審核日期

2018-7-9

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