The rapid development of an unmanned aerial vehicle (UAV) has brought new opportunities for wireless communication and edge computing. In this article, we investigate the scenario where multiple UAVs serve as edge computing devices for the Internet of Vehicles (IoV). Regardless of the allocation of computing resources, we focus on bandwidth allocation and trajectory control to maximize the communication capacity of the system so that the UAV edge computing network can process more data. With this intent, a UAV-assisted IoV edge computing system model is constructed as a nonconvex optimization problem, aiming to maximize the achievable channel capacity of the network. To solve this problem, two “quasi-distributed” multiagent algorithms, i.e., actor-critic mixing network (AC-Mix) and multi-attentive agent deep deterministic policy gradient (MA2DDPG), are proposed based on deep deterministic policy gradient. Specifically, AC-Mix utilizes a mixing network to obtain a global $Q$-value for better evaluation of joint action, while MA2DDPG employs a multihead attention mechanism to achieve multiagent collaboration. Using multi-agents deep deterministic policy gradient (MADDPG) as benchmark, several experiments are carried out to verify the performance of the proposed algorithms. Simulation results show that the convergence velocity of AC-Mix and MA2DDPG is improved by 30.0% and 63.3%, respectively, compared with MADDPG.

Deep neural networks provide intelligent solutions for Automatic Modulation Classification (AMC) tasks in the field of communication. However, their susceptibility to adversarial examples due to the interpretability problem presents a challenge as it leads to anomalous decisions. Emerging studies suggest that the high-frequency constituents within signals constitute a fundamental source of adversarial vulnerability. To address this issue, this paper introduces a Homomorphic Filtering Adversarial Defense (HFAD) algorithm that aims to effectively defend against adversarial examples by applying frequency domain filtering on the signal. This approach enhances the security and reliability of the AMC model by attenuating high-frequency components of the signal through homomorphic filtering, thereby reducing errors caused by adversarial perturbations on model outputs. The robustness of the AMC model is further enhanced through the integration of HFAD with data augmentation strategies. Experimental results demonstrate that the proposed defense algorithm not only maintains high signal recognition accuracy but also preserves communication signal transmission quality. Moreover, HFAD effectively withstands a wide range of white-box adversarial attacks and demonstrates resilience against black-box adversarial attacks, thereby enhancing the robustness of the AMC model against adversarial examples and exhibiting strong transfer performance.

Radio frequency fingerprinting (RFF) serves as a distinctive hardware trait in transmitters, forming the corner-stone of transmitter identification. While recent advancements led to significant improvements in identification accuracy, these developments also inadvertently simplify the process for adversaries to detect our transmitters. This vulnerability is particularly concerning in secure communications, as the exposure of device information could potentially result in the compromise of communication content, posing significant security threats. To counteract such risks and safeguard transmitters against unauthorized identification, this paper proposes a novel RFF concealment (RFFC) method based on IQ imbalance compensation and digital pre-distortion (DPD) techniques. This method not only effectively conceals the RFF, preventing malicious detection of the transmitter, but also enhances the system’s linearization performance. The effectiveness of the proposed RFFC framework is validated through MATLAB Simulink and a software and hardware test platform. Experimental results show that using the blind generalized linear structure-based IQ imbalance and deep neural network (DNN)-based PA nonlinearity joint concealment method performs best, reducing transmitter identification accuracy to only 17% under various signal-to-noise ratio conditions. Additionally, this method performs the best in system linearization performance.

Accurate crowd flow prediction has gained increasing importance for the development of social Internet-of-Things (IoT) systems. In this article, we provide an efficient crowd flow prediction for social IoT systems in urban space based on the mobile network big data. In particular, the usage detail records (UDRs) are used in the prediction. The feasibility of using UDRs in the prediction is first analyzed. Then, a graph data model is exploited to record and represent the mobile behavior of users. In particular, we propose to apply the heterogeneous information network (HIN) representing the UDR data and characterize the users’ behavior through the embedding methods of HIN. Moreover, an attention-based spatiotemporal graph convolution network with embedded vectors (EA-STGCN) is proposed for the final prediction. Through experimental evaluation, the advantages of the proposed model are shown in comparison to benchmarks.

Deep learning (DL), renowned for its superior feature extraction capabilities, has remarkably succeeded in specific emitter identification (SEI), especially when supported by high-quality labeled data. However, obtaining accurate signal labels in complex electromagnetic environments is challenging, and manual labeling is prone to errors, underscoring the need for robust DL-based SEI methods that can handle label noise. These methods prevent neural networks from overfitting noisy labels, thereby boosting identification performance. Yet, research in this area is still limited. Our study introduces a robust label-noise SEI approach and the sample selection and regularization (SSR) method. This involves a two-stage adaptive sample selection (ASS) driven by confidence learning. The first stage entails coarse-grained separation of true and false labels through direct deep neural network (DNN) training. In the second stage, semi-supervised learning (SSL) utilizes a regularization-inspired loss, incorporating label smoothing regularization (LSR) and entropy minimization (EM), for fine-grained sample selection. The DNN is ultimately trained on precisely selected true-labeled samples. Comparative experiments on the automatic dependent surveillance-broadcast (ADS-B) and Wi-Fi data sets demonstrate that our SSR method outperforms the existing methods in identification accuracy, particularly at a 20% label-noise ratio, achieving 86.00% accuracy with the ADS-B data set, and 99.38% with the Wi-Fi data set. The code is available at: https://github.com/sleepeach/SSR-SEI .