By exploiting the inherent hardware characteristics of wireless devices, radio frequency fingerprint identification (RFFI) has been widely applied in device authentication and spoofing attack detection to improve the security. However, due to the dependence on the training sample size, the state-of-the-art deep learning (DL)-based identification methods will face serious overfitting problem with inadequate training samples. Besides, in noncooperative scenarios, most existing methods cannot tackle the challenge of zero-shot identification, i.e., identifying the objects outside of the training data with no prior samples, which obstructs their practical applications. To solve these two problems, in this article, we propose a novel identification method that combines the deep neural network (DNN) and the unsupervised clustering. In this design, after offline training, the triplet loss convolutional neural network (TLCNN) can be utilized to extract the features of the radio frequency signals outside of the training set. Then, the$K$-means++ clustering algorithm is applied to the extracted features to realize zero-shot RFFI. Moreover, to improve the identification performances of the proposed design under small sample conditions, the random integration (RI) augmentation and the variational mode decomposition (VMD) are exploited to preprocess the input signals, and a$K$-value estimation method for the$K$-means++ clustering is proposed to ensure the effectiveness of the proposed design. Experiment on digital mobile radio (DMR) portable radios validates that the proposed design can achieve the best identification performances and the minimal time and space costs compared with the benchmark schemes.

The noises embedded in signals will degrade the signal processing quality. Traditional denoising algorithms might not work in practical systems since the statistical characteristics of noises might not be learned. To address this issue, we propose an efficient residual shrinkage convolutional neural network (RSCNN) aided denoiser based on the principle of the domain transformation, shrinking and inverse transforming operations conducted by the traditional denoiser. The proposed RSCNN is composed by the batch normalization layer, domain transformation layers, the shrinkage module and inverse transformation layers, wherein transformation layers consist of convolutional layers and the nonlinear activation function. Moreover, we propose a thresholds learning subnetwork to automatically determine the thresholds, so as to enhance noise suppressing performances. Furthermore, we compose the data set by preprocessing the received signals, and design the loss function according to different denoising requirements. To validate the efficiency and universality of the RSCNN aided denoiser, we apply the proposed RSCNN denoiser to three different application scenarios, including the modulation recognition, detection and decoding. After the offline training, at the online deployment stage, we utilize the RSCNN denoiser to reduce the noise power and improve the signal to noise ratios. Simulation results demonstrate that the proposed intelligent denoiser can efficiently improve the signal processing capabilities to achieve higher modulation recognition accuracy, better detection and decoding performances with lower complexity than benchmark schemes.

To meet the requirement of very-high-rate-data transmission over wide bandwidths, carrier aggregation (CA) has been regarded as an important technology for Long Term Evolution-Advanced (LTE-A). It considers both primary component carrier (PCell) and secondary component carrier (SCell) and many operations are based on the PCell. In this paper, the load balancing performance of dynamic SCell measurement period relaxing in the LTE-A system with CA has been discussed. Furthermore, it gives the result of SCell measurement period relaxing window under different user equipment speed which is both energy-saving and harmless to the system performance.

To meet the requirement of very-high-rate-data transmission over wide bandwidths, carrier aggregation (CA) has been regarded as an important technology for Long Term Evolution-Advanced (LTE-A). It considers both primary component carrier (PCell) and secondary component carrier (SCell) and many operations are based on the PCell. In this paper, the load balancing performance of dynamic SCell measurement period relaxing in the LTE-A system with CA has been discussed. Furthermore, it gives the result of SCell measurement period relaxing window under different user equipment speed.