在軍事領域中，如何獲取敵方通訊內容一直是各國投入大量人力與心血研究的課題，獲取敵方通訊內容的第一步，便是將截收到之訊號進行調變分類，後續才能進一步地研究如何破譯並取出其內容，然而現今訊號調變方式越來越多元，如何將訊號快速準確地進行分類，儼然成為一個重要的研究項目。
傳統訊號調變分類方式，仰賴人工運用複雜運算進行特徵擷取，再依照不同特徵進行調變分類，本文透過類神經網路特有的自我學習來進行特徵擷取與分類，跳脫自行擷取所需的複雜運算，將時間與精神專注於類神經網路演算法的改良，提升調變分類的準確度。
本文提出以卷積長暫態記憶神經網路進行調變分類技術研究，將現有之神經網路分別從訓練模型與資料集等2個部分進行改良，提出之改良型卷積長暫期記憶神經網路具有強大的抗雜訊能力與細部特徵擷取能力，經過各式不同的測試，此模型整體調變分類成功率可達64.7%，在訊雜比為0dB~20dB的範圍內，調變分類成功率可以達到90.1%，高訊雜比(+18dB)成功率可達90%，有不錯的效果。

Modulation classification is usually the first step of a major communications problem with military applications. We have to know the modulation types before we decode the signals and get the content. As the modulation types increase rapidly, automatic modulation recognition becomes an important topic which is worth researching into.
Traditionally, we use manual feature selection to get the features and do the classification. In this article, we aim to use of DL to learn from data, extract features and classify signals automatically. We will concentrate on modifying the model of DL to improve the classification accuracy.
This paper proposes a research of modulation classification using fully connected dep neural networks with convolutional long short-term memory. We modify the existing model by improving the training model and dataset. The overall classification accuracy of the modified model is 64.7%. In high SNR region(0dB~20dB), the classification accuracy is 90.1%. In high SNR(+18dB), the classification accuracy is 90%

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