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基于多端CNN的通信信號自動(dòng)調制識別研究
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新疆工程學(xué)院 信息工程學(xué)院 通信教研室

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Research on Automatic Modulation Recognition of Communication Signals Based on Multi terminal CNN
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    摘要:

    為有效解決通信信號自動(dòng)調制識別的調制類(lèi)型識別率低和調制強度識別誤差高的問(wèn)題,研究了基于多端CNN的通信信號自動(dòng)調制識別方法。根據不同類(lèi)型調制方法的作用原理,設置通信信號調制識別標準。考慮通信信號的傳輸過(guò)程,構建通信信號模型,利用帶通采樣工具采集初始通信信號,通過(guò)小波消噪、歸一化等步驟,完成初始信號的預處理。利用多端CNN算法構建通信信號識別器,提取幅值、相位、頻率等通信信號特征參數,通過(guò)特征匹配得出信號調制類(lèi)型與強度的識別結果,實(shí)現通信信號自動(dòng)調制識別。通過(guò)與傳統識別方法的對比得出結論:綜合考慮有、無(wú)干擾兩種類(lèi)型的通信信號,優(yōu)化設計識別方法的調制類(lèi)型識別率提高了49.6%,調制強度識別誤差降低了約0.0285。

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    In order to effectively solve the problems of low recognition rate of modulation type and high recognition error of modulation intensity in automatic modulation recognition of communication signals, an automatic modulation recognition method of communication signals based on multi terminal CNN was studied. According to the principle of different types of modulation methods, the communication signal modulation identification standard is set. Considering the transmission process of the communication signal, the communication signal model is constructed, and the initial communication signal is collected by using the bandpass sampling tool. The preprocessing of the initial signal is completed through wavelet denoising, normalization and other steps. The multi terminal CNN algorithm is used to build a communication signal recognizer, extract the amplitude, phase, frequency and other communication signal characteristic parameters, and obtain the recognition results of signal modulation type and intensity through feature matching, realize automatic modulation recognition of communication signals. By comparing with the traditional recognition methods, it is concluded that the recognition rate of modulation type is improved by 49.6% and the recognition error of modulation intensity is reduced by 0.0285 by considering the two types of communication signals with and without interference.

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艾散·帕合提,吾斯曼·玉山.基于多端CNN的通信信號自動(dòng)調制識別研究計算機測量與控制[J].,2023,31(8):245-250.

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歷史
  • 收稿日期:2023-01-05
  • 最后修改日期:2023-03-28
  • 錄用日期:2023-03-29
  • 在線(xiàn)發(fā)布日期: 2023-08-22
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