When the power of the mainlobe interference received by the receiver is at the same level as the power of the sidelobe interference power, the traditional eigen-projection interference suppression method has the problems of severe beam deformation and peak shift. Aiming at these problems, a beam pattern optimization method (BPOM) was proposed, which can suppress the interference well even when the mainlobe interference power is approximately equal to the sidelobe interference power. In the method, the mainlobe interference eigenvectors are firstly determined according to the correlation criterion. Then through the eigenvalue comparison, the sidelobe interference eigenvectors whose eigenvalues are approximately equal to the mainlobe interference eigenvalues are judged. After that, a projection matrix is constructed to filter out the mainlobe and sidelobe interference. Finally, the covariance matrix is reconstructed and the weight vector for beamforming is obtained. Simulation shows that BPOM has a better output performance than the existing algorithms in case that the power of the mainlobe interference is close to that of the sidelobe interference.
Aiming at the problem that the current encrypted traffic classification methods only use the single network framework such as convolutional neural network (CNN), recurrent neural network (RNN), and stacked autoencoder (SAE), and only construct a shallow network to extract features, which leads to the low accuracy of encrypted traffic classification, an encrypted traffic classification framework based on the fusion of vision transformer and temporal features was proposed. Bottleneck transformer network (BoTNet) was used to extract spatial features and bi-directional long short-term memory (BiLSTM) was used to extract temporal features. After the two sub-networks are parallelized, the feature fusion method of early fusion was used in the framework to perform feature fusion. Finally, the encrypted traffic was identified through the fused features. The experimental results show that the BiLSTM and BoTNet fusion transformer (BTFT) model can enhance the performance of encrypted traffic classification by fusing multi-dimensional features. The accuracy rate of a virtual private network (VPN) and non-VPN binary classification is 99.9%, and the accuracy rate of fine-grained encrypted traffic twelve-classification can also reach 97%.
The research purpose of this paper is focused on investigating the performance of extra-large scale massive multiple-input multiple-output ( XL-MIMO) systems with residual hardware impairments. The closed-form expression of the achievable rate under the match filter (MF) receiving strategy was derived and the influence of spatial non-stationarity and residual hardware impairments on the system performance was investigated. In order to maximize the signal-to-interference-plus-noise ratio ( SINR ) of the systems in the presence of hardware impairments, a hardware impairments-aware minimum mean squared error (HIA-MMSE) receiver was proposed. Furthermore, the stair Neumann series approximation was used to reduce the computational complexity of the HIA-MMSE receiver, which can avoid matrix inversion. Simulation results demonstrate the tightness of the derived
analytical expressions and the effectiveness of the low complexity HIA-MMSE (LC-HIA-MMSE) receiver.
A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination (JGCFD-MDL) for image classification tasks was proposed. The algorithm reveals the manifold structure of the data by learning the global constraint dictionary and introduces the Fisher discriminative constraint dictionary to minimize the intra-class dispersion of samples and increase the inter-class dispersion. To further quantify the abstract features that characterize the data, a multi-layer dictionary learning framework is constructed to obtain high-level complex semantic structures and improve image classification performance. Finally, the algorithm is verified on the multi-label dataset of court costumes in the Ming Dynasty and Qing Dynasty, and better performance is obtained. Experiments show that compared with the local similarity algorithm, the average precision is improved by 3.34% . Compared with the single-layer dictionary learning algorithm, the one-error is improved by 1.00% , and the average precision is improved by 0.54% . Experiments also show that it has better performance on general datasets.