Intelligent Reflecting Surface Assisted Secure Transmission in UAV-MIMO Communication Systems.

This paper studies the intelligent reflecting surface (IRS) assisted secure transmission in unmanned aerial vehicle (UAV) communication systems, where the UAV base station, the legitimate receiver, and the malicious eavesdropper in the system are all equipped with multiple antennas. By deploying an IRS on the facade of a building, the UAV base station can be assisted to realize the secure transmission in this multiple-input multiple-output (MIMO) system. In order to maximize the secrecy rate (SR), the transmit precoding (TPC) matrix, artificial noise (AN) matrix, IRS phase shift matrix, and UAV position are jointly optimized subject to the constraints of transmit power limit, unit modulus of IRS phase shift, and maximum moving distance of UAV.

RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning.

Signal detection is one of the most critical and challenging issues in ambient backscatter communication (AmBC) systems. In this paper, a multi-antenna AmBC signal detection method is proposed based on reconfigurable intelligent surface (RIS) and deep reinforcement learning. Firstly, an efficient multi-antenna AmBC system is developed based on RIS, which can achieve information transmission and energy collection simultaneously.

Joint Adversarial Example and False Data Injection Attacks for State Estimation in Power Systems.

Although state estimation using a bad data detector (BDD) is a key procedure employed in power systems, the detector is vulnerable to false data injection attacks (FDIAs). Substantial deep learning methods have been proposed to detect such attacks. However, deep neural networks are susceptible to adversarial attacks or adversarial examples, where slight changes in inputs may lead to sharp changes in the corresponding outputs in even well-trained networks.