Accurately identifying optical fiber vibration signals is crucial for ensuring the proper operation of optical fiber perimeter security warning systems. To enhance the recognition accuracy of intrusion events detected by the distributed acoustic sensing system (DAS) based on phase-sensitive optical time-domain reflectometer (φ-OTDR) technology, we propose an identification method that combines empirical mode decomposition (EMD) with convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. First, the EMD algorithm decomposes the collected original optical fiber vibration signal into several intrinsic mode functions (IMFs), and the correlation coefficient between each IMF and the original signal is calculated. The signal is then reconstructed by selecting effective IMF components based on a suitable threshold. This reconstructed signal serves as the input for the network. CNN is used to extract time-series features from the vibration signal and LSTM is employed to classify the reconstructed signal. Experimental results demonstrate that this method effectively identifies three different types of vibration signals collected from a real-world environment, achieving a recognition accuracy of 97.3% for intrusion signals. This method successfully addresses the challenge of φ-OTDR pattern recognition and provides valuable insights for the development of practical engineering products.

This paper investigates physical layer secure communication in a wideband wireless system, where a base station (BS) equipped with an extremely large scale antenna array (ELAA) transmits confidential information to a legitimate receiver under the threat of a potential eavesdropper. Due to the high carrier frequency and large antenna aperture, both the receiver and eavesdropper lie in the near-field region of the BS. In order to mitigate the beam split effect and reduce the hardware cost, a true-time delayer-based hybrid beamfocusing architecture is designed. Then, a nonconvex sum secrecy capacity maximization problem (SSCM) is formulated for securing wideband communications. Based on alternating optimization, the SSCM is decomposed into three subproblems solved iteratively for designing the digital beamfocusing vectors, time delay matrices, and phase shift matrices on each subcarrier, respectively. Simulation results show that the proposed scheme yields significantly high secrecy capacity compared to benchmarks, which validates the effectiveness of our scheme in enhancing secure wideband communications and mitigating the beam split effects.

This letter makes an attempt to design a user-specific reconfigurable intelligent surface (US-RIS) aided secure uplink communication scheme. In the considered scenario, the mobile terminal (MT) equipped with a US-RIS needs to transmit confidential information to the base station (BS) under the threat from the eavesdropper, while the uplink resource is reused by a device-to-device (D2D) pair to further improve the spectral efficiency. The formulated nonconvex optimization problem is to jointly design the transmit phase shifter matrixes of the US-RIS and the power control of the D2D communication for maximizing the secrecy rate as well as the data rate of the D2D communication. We then alternately optimize the phase shifter matrixes and D2D transmit power by using successive convex approximation and difference between two concave functions programming, respectively. Simulation results validate the effectiveness of our designed scheme.

To tackle the challenge of enhancing moving target detection performance in environments characterized by small sample sizes and non-uniformity, methods rooted in sparse signal reconstruction have been incorporated into Space-Time Adaptive Processing (STAP) algorithms. Given the prominent sparse nature of clutter spectra in the angle-Doppler domain, adopting sparse recovery algorithms has proven to be a feasible approach for accurately estimating high-resolution spatio-temporal two-dimensional clutter spectra. Sparse Bayesian Learning (SBL) is a pivotal tool in sparse signal reconstruction and has been previously utilized, yet it has demonstrated limited success in enhancing sparsity, resulting in insufficient robustness in local fitting. To significantly improve sparsity, this paper introduces a hierarchical Bayesian prior framework and derives iterative parameter update formulas through variational inference techniques. However, this algorithm encounters significant computational hurdles during the parameter update process. To overcome this obstacle, the paper proposes an enhanced Variational Bayesian Inference (VBI) method that leverages prior information on the rank of the temporal clutter covariance matrix to refine the parameter update formulas, thereby significantly reducing computational complexity. Furthermore, this method fully exploits the joint sparsity of the Multiple Measurement Vector (MMV) model to achieve greater sparsity without compromising accuracy, and employs a first-order Taylor expansion to eliminate grid mismatch in the dictionary. The research presented in this paper enhances the moving target detection capabilities of STAP algorithms in complex environments and provides new perspectives and methodologies for the application of sparse signal reconstruction in related fields.