Radio Frequency Fingerprint Collaborative Intelligent Identification Using Incremental Learning

Liu, Mingqian; Wang, Jiakun; Zhao, Nan; Chen, Yunfei; Song, Hao; Yu, F. Richard

doi:10.1109/tnse.2021.3103805

Cited by 42 publications

(31 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A central node is selected from all edge nodes as the fusion center for parameter fusion and output coordination to complete federated learning. In the system, the received signal can be written as [1]:…”

Section: System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Radio Frequency Fingerprint Collaborative Intelligent Blind Identification for Green Radios

Liu

Chen

et al. 2023

IEEE Trans. on Green Commun. Netw.

Self Cite

View full text Add to dashboard Cite

show abstract

Section: System Modelmentioning

confidence: 99%

“…R ADIO frequency fingerprint identification (RFFI) refers to the analysis and extraction of the characteristics in the emitted signals to identify their frequency fingerprint [1]. The characteristics of the frequency fingerprint can be divided into two types: unintentional modulation and intentional modulation.…”

Section: Introductionmentioning

confidence: 99%

Radio Frequency Fingerprint Collaborative Intelligent Blind Identification for Green Radios

Liu

Chen

et al. 2023

IEEE Trans. on Green Commun. Netw.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, signal recognition based on deep learning has become a research hotspot [28,29]. In addition, the complexity of signal recognition can be reduced by feature extraction [30]. Recognition algorithm based on Sevcik fractal dimension and energy aggregation degree obtains obvious advantages at low values of JNR, such as the fast recognition speed and high recognition rate [12].…”

Section: Cognitive Fh Strategy For Uav Swarmsmentioning

confidence: 99%

Cognitive-Based High Robustness Frequency Hopping Strategy for UAV Swarms in Complex Electromagnetic Environment

Xue

Zhao

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) confront various interference in the process of missions, and frequency hopping (FH) technology is one of the effective means of anti-interference for UAV. The FH system can avoid interference frequency points and possess certain anti-interference ability by making the carrier frequency continuously popping. However, the increasingly complex electromagnetic environment of UAV swarms requires more efficient anti-interference measure. To further improve the anti-interference ability of UAVs, this paper proposes a cognitive-based high robustness FH strategy. By adding a cognitive module to FH system, UAVs can avoid the interference frequency points adaptively and sensitively. The proposed system can identify typical suppressed interference and filter the interference frequency points. Cognitive FH system can avoid interference points completely with accurate detections and enhance the robustness for UAV swarms in complex electromagnetic environment compared to conventional FH system. Simulation results have shown that BER performance of proposed cognitive FH strategy is better than conventional FH strategy for typical suppressed interference.

show abstract

“…Communication signals are accompanied by more complicated signals such as jamming, inadvertent crosstalk, and natural thunderstorms [2][3][4], whether in the air, sea, or land. In order to adapt to the current development of the communication industry, in recent years, machine learning has been widely used to strengthen electromagnetic spectrum management and ensure the reliable transmission of wireless communication [5,6]. The construction of a complex electromagnetic environment is important for improving the adaptability of its own communication system to a specific electromagnetic environment [7], and the reconstruction of noncooperating parties' communication signals is an important part of constructing a complex electromagnetic environment [8].…”

Section: Introductionmentioning

confidence: 99%

Waveform Reconstruction of DSSS Signal Based on VAE-GAN

Feng

Zhang

Chen

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The complex electromagnetic environment will limit the efficacy of communication equipment. It is critical to construct a complex electromagnetic environment to test communication equipment in order to maximize its capability. One of the most important methods for constructing a complex electromagnetic environment is signal reconstruction. This paper proposes a VAE-GAN-based method for reconstructing direct sequence spread spectrum (DSSS) signals. In this method, the deep residual shrinkage network (DRSN) and self-attention mechanism are added to the encoder and discriminator of VAE-GAN. In feature learning, the DRSNs can reduce the redundant information caused by noise in the collected signal. The self-attention mechanism can establish the long-distance dependence between the input sequences, making it easier for the network to learn the samples’ pseudonoise (PN) sequence features. In addition, feature loss is applied to the encoder and generator to improve network stability during training. The results of the experiments indicate that this method can reconstruct DSSS signals with the characteristics of the target signal.

show abstract

Radio Frequency Fingerprint Collaborative Intelligent Identification Using Incremental Learning

Cited by 42 publications

References 36 publications

Radio Frequency Fingerprint Collaborative Intelligent Blind Identification for Green Radios

Radio Frequency Fingerprint Collaborative Intelligent Blind Identification for Green Radios

Cognitive-Based High Robustness Frequency Hopping Strategy for UAV Swarms in Complex Electromagnetic Environment

Waveform Reconstruction of DSSS Signal Based on VAE-GAN

Contact Info

Product

Resources

About