In this work, we investigate the Age of Information (AoI) in aerial intelligent reflecting surfaces (IRSs) assisted covert data collection in Internet of Things (IoT) networks. Operating with the autonomous aerial vehicle (AAV) and IRS can improve the data transmission covertness, as well as the data freshness by reconstructing the wireless propagation environment. Specifically, we consider a scenario in which ground IoT sensors transmit confidential information to the legitimate receiver (Bob), through IRS assisted AAV relay, at the same time, an eavesdropper passively listens to and intercepts the confidential information sent by sensors. To minimize the average AoI of the system, a joint AAV trajectory and IRS phase shift optimization problem is formulated under the constraints of covertness requirement. The minimum error detection probability and optimal detection threshold are first derived at Willie, which represents the worst case situation for the legitimate transmission. The constructed problem is a mixed integer programming NP-Hard problem, which is more complex using traditional convex optimization methods. Therefore, the online learning, i.e., deep reinforcement learning is leveraged to obtain the near-optimal solution. Details, the deep Q network (DQN) and deep deterministic policy gradient (DDPG) methods are utilized. Numerical results show that deep reinforcement learning can improve the information freshness of the system by reasonably designing AAV trajectory and IRS phase shift under a given covertness constraint.

Internet of Things (IoT) play a paramount role in every aspect of our daily lives. Due to more diverse human needs, the future IoT networks are expected to be highly dynamic and heterogeneous with assistance of mobile nodes, such as unmanned aerial vehicles (UAVs). However, the broadcast and openness nature of wireless communication and high-mobile characteristic of UAV can cause security threats to UAV-assisted IoT systems. For this sake, we consider exploiting covert communication to provide such a system with a higher level communication security, which can prevent the legitimate transmission being detected by the adversary monitor. Specifically, this article jointly optimizes the transmit power and hovering location of the UAV to guarantee communication security in the IoT system. We maximize the signal-to-noise ratio (SNR) of the legitimate receiver in presence of a malicious warden, with constraints of communication covertness, the UAV’s spatial location and maximum transmit power. Particularly, the UAV’s location is represented in terms of angles, rather than the commonly used distance, in most of the literature. The optimal location is determined in two steps. The simulation results show that the proposed optimization schemes can effectively find the optimal hovering location and transmit power of the UAV to maximize the receiver’s SNR under the covertness constraint. The optimal hovering location is directly above the line connecting the legitimate receiver and the warden, and in close proximity within the small region directly above the receiver, which has implications for other analogous research scenarios or practical applications of UAV.

Automatic modulation recognition (AMR) of radio signal is an important research topic in the area of non-cooperative communication and cognitive radio. Recently deep learning (DL) techniques enable significant progress in AMR. However, the techniques of adversarial machine learning cause the threats of adversarial attacks in DL-based AMR. In this paper, we aim to make AMR model robust, accurate and lightweight, thus propose a multi-distillation mechanism for robust training of DL-based AMR models, namely Adversarial Multi-Distillation (AMD). In the framework of AMD, by knowledge distillation, two powerful teacher models transfer the learned classification knowledge and defense knowledge, respectively, to the student model to form robust training. Our experiments with public dataset RML2016.10a show that the proposed method can significantly improve the defense of AMR models to against adversarial perturbations and keep relatively high classification accuracy, which enables robust decision making with lightweight models under adversarial attacks.

A wireless communications system usually consists of a transmitter which transmits the information and a receiver which recovers the original information from the received distorted signal. Deep learning (DL) has been used to improve the performance of the receiver in complicated channel environments and state-of-the-art (SOTA) performance has been achieved. However, its robustness has not been investigated. In order to evaluate the robustness of DL-based information recovery models under adversarial circumstances, we investigate adversarial attacks on the SOTA DL-based information recovery model, i.e., DeepReceiver. We formulate the problem as an optimization problem with power and peak-to-average power ratio (PAPR) constraints. We design different adversarial attack methods according to the adversary’s knowledge of DeepReceiver’s model and/or testing samples. Extensive experiments show that the DeepReceiver is vulnerable to the designed attack methods in all of the considered scenarios. Even in the scenario of both model and test sample restricted, the adversary can attack the DeepReceiver and increase its bit error rate (BER) above 10%. It can also be found that the DeepReceiver is vulnerable to adversarial perturbations even with very low power and limited PAPR. These results suggest that defense measures should be taken to enhance the robustness of DeepReceiver.

In cognitive radio networks, providing accurate recognition of the primary user’s signal is great important for designing the spectrum access strategies. Source number estimation has served as a key fundamental technique to facilitate the signal recognition in the mixed received signals scenario. In this paper, we propose a deep learning based source number estimation method under the single-channel conditions. The architecture of the network is first designed. Then, the received complex signals are reconstructed as in-phase and quadrature (IQ) data in order to adapt to the convolutional neural network for extracting the deep features. Moreover, the cost function that is used to train the proposed network is properly designed by exploring the maximum likelihood function. Supervised training is performed to generate a classifier that can identify the number of sources in the mixtures. The proposed deep leaning-based source number estimation method is tested by experiments on simulation signals and actual signals. The results revealed the effectiveness of the proposed method in solving the problem of single-channel source number estimation compared to the conventional methods.