Name
Abstract | ||
|---|---|---|
In the era of 5G and Internet of Things, the number of connected devices has increased dramatically, which has placed a heavy burden on the network. So it is worthwhile to study the intelligent and accurate identification and control of devices. Radio Frequency (RF) fingerprinting technology has been widely used in wireless device identification. RF fingerprint contains rich nonlinear characteristics that reflect the uniqueness of wireless devices. However, redundant or irrelevant information in the features will result in poor recognition performance. In response to the problem, a novel integration feature selection method is proposed in this paper. The principle is to improve the identification performance through extracting the best-performing feature subset from the initial features. Signals from seven power amplifiers are collected as the dataset. The covariance feature is extracted as RF fingerprint and K-Nearest Neighbor (KNN) classifier is used for classification. The stability of the proposed method is evaluated by the Spearman correlation coefficient. The robustness is evaluated under the varying Signal-to-Noise Ratio (SNR). The identification results demonstrate the excellent performance of the proposal. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/VTC2020-Spring48590.2020.9129105 | VTC Spring |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Li | 1 | 0 | 0.34 |
| Yun Lin | 2 | 81 | 14.47 |
| Zheng Dou | 3 | 0 | 0.34 |
| Yifan Chen | 4 | 58 | 19.82 |