訊號由於通訊設備其硬體製造過程中的微小差異而在實體層中所帶有之唯一特徵，即為射頻指紋特徵(Radio Frequency Fingerprint, RFF)。人們不僅可以利用其唯一且難以仿冒之特性，在通訊的各種資安問題之下得到保障，也讓科技發展拓展出更多的可能性。
然而，射頻指紋特徵卻會因為無線通道或個別接收端差異等等的實際訊號傳輸過程的變因而產生影響，進一步的降低其辨識率；因此，射頻指紋特徵的提取、消除通道對射頻指紋特徵之影響，抑或是提高射頻指紋特徵可攜性之相關研究近年來都十分受到關注。
本論文主要是利用網卡與軟體無線電設備(Universal Software Radio Peripheral, USRP)做為訊號傳送端與接收端進行實驗數據之量測，並且於接收封包內的特定符元中提取射頻指紋特徵，以完成後續辨識設備之目的；但如上個段落所述，在實際量測的環境之下存在各種會導致辨識結果下降的變因，而本論文將針對實驗量測誤差所造成的影響進行改善，藉由分析不同原始資料的分布以及特徵變化，並進一步對其進行篩選，盡可能移除因環境變化或是實驗量測誤差而產生訊號偏差的封包，從而提升最終射頻指紋特徵之辨識率。
訊號由於通訊設備其硬體製造過程中的微小差異而在實體層中所帶有之唯一特徵，即為射頻指紋特徵(Radio Frequency Fingerprint, RFF)。人們不僅可以利用其唯一且難以仿冒之特性，在通訊的各種資安問題之下得到保障，也讓科技發展拓展出更多的可能性。
然而，射頻指紋特徵卻會因為無線通道或個別接收端差異等等的實際訊號傳輸過程的變因而產生影響，進一步的降低其辨識率；因此，射頻指紋特徵的提取、消除通道對射頻指紋特徵之影響，抑或是提高射頻指紋特徵可攜性之相關研究近年來都十分受到關注。
本論文主要是利用網卡與軟體無線電設備(Universal Software Radio Peripheral, USRP)做為訊號傳送端與接收端進行實驗數據之量測，並且於接收封包內的特定符元中提取射頻指紋特徵，以完成後續辨識設備之目的；但如上個段落所述，在實際量測的環境之下存在各種會導致辨識結果下降的變因，而本論文將針對實驗量測誤差所造成的影響進行改善，藉由分析不同原始資料的分布以及特徵變化，並進一步對其進行篩選，盡可能移除因環境變化或是實驗量測誤差而產生訊號偏差的封包，從而提升最終射頻指紋特徵之辨識率。

Because of the slight difference in the hardware manufacturing process of the communication device, the communication signal brings unique feature in the physical layer, which is called the radio frequency fingerprint (RFF). Therefore, ¬we can take advantage of its unique and difficult-to-counterfeit feature to improve communication information security issues and expand new technological developments.
However, the features of the radio frequency fingerprint can be affected by the changes in the actual signal transmission process, such as various wireless channels or different receivers, which further reduces the RFF recognition rate.
Thus, the techniques including the extraction of the RFF features, the elimination of the channel influence to the RFF, enhancement of the RFF portability have attracted much attention recently.
This work uses wireless local area network adaptors and software radio equipment (Universal Software Radio Peripheral, USRP) as the signal transmitters and receivers to measure the experimental data. The RFF features are extracted from some specific symbols in the received packet within measurement data. The transmitters are identified based on the extracted RFF features.
There are various factors that will cause the degradation of identification rate under the actual measurement environment. This work improves the degradation caused by possible imperfection of experimental measurement by filtering unqualified communication packages based on the analysis of measurement raw data and the distribution of extracted features.
The experimental results validate the effectiveness of the data selection process as it improves the transmitter identification rate successfully.

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