近年來無線通訊技術發展快速並且日漸普及，在日常生活中已成為一種普遍的通訊方式。然而無線通訊藉由電磁波傳遞的特性，使得通訊範圍內的使用者皆可參與通訊，進而對訊號造成影響。因此在無線通訊發展帶來了便利的同時，卻也在安全議題上產生了隱憂。
近幾年在物理層中發展出的裝置指紋特徵辨識技術，為一種難以仿冒且可靠的識別技術。但同時指紋特徵含有缺乏可攜性的問題，使得相異裝置所提取之特徵無法通用，每台接收裝置皆須自行提取特徵並訓練模型才能進行傳送端辨識，進而導致技術難以應用至實際產品中。因此如何改善指紋特徵可攜性問題成為一項重大課題。
本篇論文提出一種改善指紋特徵可攜性的校正方法，該方法為先透過調整訊號分布之三、四階動差，令分布接近非偏斜分布，而後再進行一、二階動差調整以去除大部分的接收端差異，令訊號無關於接收端差異影響。如此一來透過處理後之訊號以提取指紋特徵則可以使特徵可攜性提高，並令準確率提升。

In recent years, since wireless communication technology has developed rapidly, it
turns into a common way of communication nowadays. However, the development of wireless communication technology brings both convenience and the potential threat of
information security at the same time. Wireless communication is transmitted by electromagnetic waves, so users in the same communication range can have their own
communications simultaneous and may interfere other users' communications.
Recent research has found that RF fingerprint identification in the physical layer is the key to increasing communication security as it is reliable and can hardly be counterfeited. On the other side, the lack of portability of RF fingerprints causes the inaccessibility of using the same features extracted by one receiver at another receiver. Thus each receiver must extract its features for training models before identifying the transmitter, which causes difficulties in applying the technique on production. Therefore, improving RF fingerprint portability becomes a significant issue.
In this work, we propose a calibration method to improve the RF fingerprint
portability. Firstly, we calibrate the skewness and kurtosis of received signals to make it close to the non-skewed distribution, and then calibrate the mean and variance of received signals to remove the differences between each receiver. Experimental results show that the proposed method can improve feature portability and increase the accuracy of RF fingerprint identification.

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