In this paper, given the beamforming vector of confidential messages and the artificial noise (AN) projection matrix and total power constraint, a power allocation(PA) strategy of maximizing secrecy rate (Max-SR) is proposed for secure directional modulation networks. By the method of Lagrange multiplier, the analytical expression of the proposed PA strategy is derived. To confirm the benefit from the Max SR-based PA strategy, we take all the null-space projection beamforming scheme as an example and derive its closed-form expression of optimal PA strategy. From simulation results, we find the following facts: in the medium and high signal-to-noise ratio (SNR) regions. compared with three typical PA parameters such β = 0.1, 0.5, and 0.9, the optimal PA shows a substantial SR performance gain with maximum gain percent up to more than 60%. In addition, as the PA factor increases from 0 to 1, the achievable SR increase accordingly in the low SNR region, whereas it first increases and the decreases in the medium and high SNR regions, where the SR can be approximately viewed as a convex function of the PA factor. Finally, as the number of antennas increases, the optimal PA factor become large and tends to one in the medium and high SNR regions. In other words, the contribution of AN to SR can be trivial in such a situation.

Cognitive radio (CR) is regarded as the key technology of the 6th-Generation (6G) wireless network. Because 6G CR networks are anticipated to offer worldwide coverage, increase cost efficiency, enhance spectrum utilization, and improve device intelligence and network safety. This article studies the secrecy communication in an energy-harvesting (EH)-enabled Cognitive Internet of Things (EH-CIoT) network with a cooperative jammer. The secondary transmitters (STs) and the jammer first harvest the energy from the received radio frequency (RF) signals in the EH phase. Then, in the subsequent wireless information transfer (WIT) phase, the STs transmit secrecy information to their intended receivers in the presence of eavesdroppers while the jammer sends the jamming signal to confuse the eavesdroppers. To evaluate the system secrecy performance, we derive the instantaneous secrecy rate and the closed-form expression of secrecy outage probability (SOP). Furthermore, we propose a deep reinforcement learning (DRL)-based framework for the joint EH time and transmission power allocation problems. Specifically, a pair of ST and jammer over each time block is modeled as an agent which is dynamically interacting with the environment by the state, action, and reward mechanisms. To better find the optimal solutions to the proposed problems, the long short-term memory (LSTM) network and the generative adversarial networks (GANs) are combined with the classical DRL algorithm. The simulation results show that our proposed method is highly effective in maximizing the secrecy rate while minimizing the SOP compared with other existing schemes.

Rapid identification of peanut seed quality is crucial for public health. In this study, we present a terahertz wave imaging system using a convolutional neural network (CNN) machine learning approach. Terahertz waves are capable of penetrating the seed shell to identify the quality of peanuts without causing any damage to the seeds. The specificity of seed quality on terahertz wave images is investigated, and the image characteristics of five different qualities are summarized. Terahertz wave images are digitized and used for training and testing of convolutional neural networks, resulting in a high model accuracy of 98.7% in quality identification. The trained THz-CNNs system can accurately identify standard, mildewed, defective, dried and germinated seeds, with an average detection time of 2.2 s. This process does not require any sample preparation steps such as concentration or culture. Our method swiftly and accurately assesses shelled seed quality non-destructively.

Cognitive Radio (CR) and Energy Harvesting (EH) techniques have offered insights to mitigate issues related to inefficient spectrum utilization and limited energy storage capacity. In Cognitive Radio Networks, security threats, particularly from eavesdroppers, may result in information leakage. This study focuses on enhancing the Physical Layer Security (PLS) of multi-users with EH by employing cooperative jamming via a Unmanned Aerial Vehicle (UAV) to maximize the secure communication rate. In the UAV-assisted EH-CR system, Secondary Users (SUs) can utilize the licensed spectrum band occupied by a Primary User (PU) if the cooperative jamming power from SUs to the PU remains below a certain threshold. SUs can harvest and use Radio Frequency (RF) energy from the Primary Transmitter (PT). The UAV jammer disrupts the eavesdropper by transmitting jamming signals, thereby minimizing stolen information to optimize long-term secure communication performance. The paper formulates the problem of maximizing the average secure communication rate while considering system constraints and jointly optimizes the UAV trajectory, transmission power, and EH coefficient. As the problem is non-convex, it is reformulated as a Markov Decision Process (MDP). The paper employs the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to address the problem, introduces counterfactual baselines to tackle the credit assignment problem in centralized learning, and integrates the Long Short-Term Memory (LSTM) network to enhance the learning capability of sequential sample data, thereby improving the training efficiency and effectiveness of the algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed method in maximizing the system's secure communication rate.

This paper proposes a novel Graph Recurrent Neural Network (GRNN) based approach for detecting eavesdropping attacks in smart grid wireless communication systems enabled by Simultaneous Wireless Information and Power Transfer (SWIPT). By leveraging the graph-centric nature of GRNNs, the proposed method effectively learns the topological structure and edge features of Wireless Sensor Networks (WSNs), enabling the detection of eavesdropping attacks in dynamic WSNs. This paper mathematically models the Channel State Information (CSI) under man-in-the-middle eavesdropping attacks based on Physical Layer Security (PLS) in SWIPT networks. Moreover, this paper sets up a real-world testbed to create training and testing datasets. The proposed GRNN model can handle large-scale complex topologies and dynamic eavesdropping networks, accurately detect eavesdropping behaviors, and enhance the security of information transmission in WSNs. Simulation results demonstrate that, compared with algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), Graph Convolutional Network (GCN), and Gated Recurrent Unit (GRU), the proposed method exhibits stronger robustness under complex attack scenarios, achieving a detection accuracy of over 95%. This paper provides a novel and effective graph learning solution for smart grid wireless communication security, which is of great significance to ensure the stable and reliable operation of smart grids.

This paper presents a novel timing synchronization scheme for orthogonal frequency-division multiplexing communication systems by leveraging the phase shift caused by the timing error and the power difference between subcarriers. The proposed scheme is applied in the physical layer of vehicular ad hoc networks using the IEEE 802.11p standard. The proposed work joints three different estimators to present a robust synchronizer. The results demonstrate that the proposed estimators greatly improve the performance of the communication system and outperform other approaches in terms of the mean square error and the bit error rate. The estimators can either work as totally blind or data-aided using the preamble field of the IEEE 802.11p.