Reconfigurable Intelligent Surface (RIS) is one of the promising techniques for 6G wireless communications, and recently has also been shown to be able to improve secure communications. However, there is a “double fading” effect in the reflection link between base station and user, thus passive RIS only achieves a negligible secrecy gain in typical communications scenarios. In this letter, we propose an active RIS-aided multi-antenna physical layer secrecy transmission scheme, where the active RIS can amplify the signal actively. Our aim is to minimize the transmit power subject to the constraint of secrecy rate. To solve the non-convex optimization problem, a penalty-based alternating minimization (AltMin) algorithm is proposed to optimize both the beamformer at the transmitter and the reflection matrix at RIS. Simulation results show that active RIS can resist the impact of “double fading” effect effectively, and is more energy efficient than passive RIS.

In this study, we focus on the Multi-robot Coverage Path Planning (MCPP) problem for maritime Search And Rescue (SAR) missions using a multiple Autonomous Underwater Vehicle (AUV) system, with the ultimate purpose of efficiently and accurately discovering the target from sonar images taken by Side-Scan Sonar (SSS) mounted on the AUVs. Considering the specificities of real maritime SAR projects, we propose a novel MCPP method, in which the MCPP problem is transformed into two sub-problems: Area partitioning and single-AUV coverage path planning. The structure of the task area is first defined using Morse decomposition of the spike pattern. The area partitioning problem is then formulated as an AUV ordering problem, which is solved by developing a customized backtracking method to balance the workload and to avoid segmentation of the possible target area. As for the single-AUV coverage path planning problem, the SAR-A* method is adopted, which generates a path that preferentially visits the possible target areas and reduces the number of turns to guarantee the high quality of the resulting sonar images. Simulation results demonstrate that the proposed method can maintain the workload balance and significantly improve the efficiency and accuracy of discovering the target. Moreover, our experimental results indicate that the proposed method is practical and the mentioned specificities are useful for discovering targets.

This paper addresses the problem of localization accuracy degradation caused by outliers of the angle of arrival (AOA). The problem of outlier detection of the AOA is converted into the detection of the estimated source position sets, which are obtained by the proposed division and greedy replacement method. The Mahalanobis distance based on robust mean and covariance matrix estimation method is then introduced to identify the outliers from the position sets. Finally, the weighted least squares method based on the reliable probabilities and distances is proposed for source localization. The simulation and experimental results show that the proposed method outperforms representative methods when unreliable AOAs are present.

Environmental sound recognition (ESR) is an emerging research topic in audio pattern recognition. Many tasks are presented to resort to computational models for ESR in real-life applications. However, current models are usually designed for individual tasks, and are not robust and applicable to other tasks. Cross-task models, which promote unified knowledge modeling across various tasks, have not been thoroughly investigated. In this paper, we propose a cross-task model for three different tasks of ESR: acoustic scene classification, urban sound tagging, and anomalous sound detection. An architecture named SE-Trans is presented that uses attention mechanism-based Squeeze-and-Excitation and Transformer encoder modules to learn channel-wise relationship and temporal dependencies of the acoustic features. FMix is employed as the data augmentation method that improves the performance of ESR. Evaluations for the three tasks are conducted on the recent databases of DCASE challenges. The experimental results show that the proposed cross-task model achieves state-of-the-art performance on all tasks. Further analysis demonstrates that the proposed cross-task model can effectively utilize acoustic knowledge across different ESR tasks.

This letter considers the problem of robust angle of arrival (AOA) source localization in the presence of correlated noise and outliers based on nonconvex sparse optimization. To maintain the total convexity of the cost function while using the nonconvex penalty, we propose to regularize the correlation matrix weighted cost by generalized minimax concave (GMC) function. Then the alternating direction forward-backward splitting algorithm (ADFBS) is proposed to estimate outliers and source position simultaneously. To counter the bias problem of ADFBS, a new ADFBS based instrumental-variable estimator (IVADFBS) is developed. The IVADFBS is observed to produce nearly unbiased estimates with lower mean squared errors.

This paper investigates an intelligent reflecting surface (IRS) aided green multiple-user downlink communication system. In contrast to the existing works that deploy the IRS in a fixed location, the location of the IRS is taken as an optimization variable to minimize the total transmit power by jointly optimizing the location of the IRS, transmit beamformers at the base station (BS), and IRS phase shifts. We point out a critical conclusion that before and after IRS deployment, the channel state information (CSI) of all the communication terminals is different, so an offline-online hybrid-CSI optimization framework is proposed to solve the problem. In the offline stage, we optimize the IRS location with only the statistical CSI (S-CSI) so the ergodic quality of service (QoS) constraints have to be considered, and universal lower bounds associated only with the location variable are derived to decouple all variables. In the online stage, all the instantaneous-CSI (I-CSI) are available. To solve this non-convex problem, an alternating optimization framework is developed. We propose a Riemannian Manifold (RM) algorithm to optimize the IRS phase shifts. Simulation results validate that the proposed algorithm is convergent and effective, and show that the location deployment of IRS is crucial for green communication.