With the development of the Internet of Things (IoT), the IoT devices are increasing day by day, resulting in increasingly scarce spectrum resources. At the same time, many IoT devices are facing inevitable malicious attacks. The cognitive Radio-enabled IoT (CR-IoT) is proposed as an effective method for spectrum resource allocation and risk monitoring in the IoT. The signal detection and modulation recognition are the key technologies for CR-IoT, addressing the problem of multisignal detection and automatic modulation classification (AMC) is one of the prerequisites for realizing secure dynamic spectrum access. Based on sliding window and deep learning (DL), this study proposes a multisignal frequency domain detection and recognition method. The frequency spectrum of the time-domain overlapping signal is obtained through the fast Fourier transform (FFT), and the frequency spectrum is segmented based on the signal energy detection method. Finally a complex convolutional neural network (CNN) is constructed for the identification of signal spectrum information. The proposed method can recognize 264 time-domain aliasing and frequency-closed signals with an accuracy of 97.3% under the influence of −2 dB corresponding to the noise of the calibration signal. In addition, the proposed method eliminates the influence of bandwidth, which can effectively detect and recognize the signal types of each component in the frequency band. This method has wide applicability and provides an effective scheme for the IoT cognitive technology.

In the field of Internet of Things (IoT), Unmanned Aerial Vehicle (UAV) swarms are widely used to assist IoT communication due to their simple deployment, high mobility and high cost effectiveness to help improve IoT network coverage and topological flexibility. However, due to the openness of UAV swarm network, UAV nodes are vulnerable to malicious attacks and fail, especially some key nodes, which will seriously degrade the network performance. To solve this problem, a key node identification model based on Graph Convolutional Networks (GCN), named KNIGCN, is proposed. Specifically, the topology model of UAV cluster network is firstly built based on Flying Ad Hoc Network (FANET) communication architecture and Dijkstra’s algorithm, then node criticality labels in the network are made by means of traffic statistics, and finally KNIGCN model is used to predict the criticality scores of each node and sort them so as to screen out a certain number of key nodes. The experimental results show that the model has good performance of key node identification. Compared with traditional methods, the proposed method makes a more reasonable judgment of node criticality, and the key nodes identified have a more important impact on network performance. Moreover, the proposed scheme can effectively improve the identification speed of key nodes in large-scale, complex or dynamically changing topology of UAV cluster networks. It can provide an effective scheme for maintaining the security and stability of UAV swarm network communication.

Unmanned aerial vehicles (UAVs), relying on wireless communication, are inevitably influenced by the complex electromagnetic environment, attributed to the development of wireless communication technology. The modulation information of signals can assist in identifying device information and interference in the environment, which is significant for UAV communication environment monitoring. Therefore, in scenarios involving the communication of UAVs, it is necessary to find out how to perform the spectrum monitoring method to obtain the modulation information. Most existing methods are unsuitable for scenarios where multiple signals appear in the same spectrum sequence or do not use an end-to-end structure. Firstly, we established a spectrum dataset to simulate the UAV communication environment and developed a label method. Then, detection networks were employed to extract the presence and location information of signals in the spectrum. Finally, decision-level fusion was used to combine the output results of multiple nodes. Five modulation types, including ASK, FSK, 16QAM, DSB-SC, and SSB, were used to simulate different signal sources in the communication environment. Accuracy, recall, and F1 score were used as evaluation metrics. The networks were tested at different signal-to-noise ratios (SNRs). Among the different modulation types, FSK exhibits the most stable recognition performance across different models. The proposed method is of great significance for wireless radio spectrum monitoring in complex electromagnetic environments and is adaptable to scenarios where multiple receivers are used in vast terrains, providing a deep learning-based approach to radio monitoring solutions for UAV communication.

Multi-unmanned aerial vehicle (UAV) systems are crucial for establishing resilient communication networks in disaster-stricken areas, but their limited energy and dynamic characteristics pose significant challenges for sustained and reliable service provision. Optimizing resource allocation in this situation is a complex sequential decision-making problem, which is naturally suitable for multi-agent reinforcement learning (MARL). However, the most advanced MARL methods (e.g., multi-agent proximal policy optimization (MAPPO)) often encounter difficulties in the “loosely coupled” multi-UAV environment due to their overly centralized evaluation mechanism, resulting in unclear credit assignment and inhibiting personalized optimization. To overcome this, we propose a novel hierarchical framework supported by MAPPO with decoupled critics (MAPPO-DC). Our framework employs an efficient clustering algorithm for user association in the upper layer, while MAPPO-DC is used in the lower layer to enable each UAV to learn customized trajectories and power control strategies. MAPPO-DC achieves a complex balance between global coordination and personalized exploration by redesigning the update rules of the critic network, allowing for precise and personalized credit assignment in a loosely coupled environment. In addition, we designed a composite reward function to guide the learning process towards the goal of proportional fairness. The simulation results show that our proposed MAPPO-DC outperforms existing baselines, including independent proximal policy optimization (IPPO) and standard MAPPO, in terms of communication performance and sample efficiency, validating the effectiveness of our tailored MARL architecture for the task. Through model robustness experiments, we have verified that our proposed MAPPO-DC still has certain advantages in strongly coupled environments.

Inferring the topology of Wireless Communication Networks (WCNs) from radiated electromagnetic signals is crucial for network layer radio management. This study analyzes WCN Topology Inference (TPI) mechanism and derives that the topology information between two nodes is distributed across their own signals and those of neighboring nodes. Existing WCN-TPI methods typically utilize a fraction of the topological information from two-node signals, limiting their performance. To address this challenge, this study proposes a multi-node TPI method, highlighting its advantages in terms of interference signal, sequential feature, and information quantity. Additionally, the lower bound of the exact probability for TPI is determined using Acknowledge feature, whereas the impact of factors such as node degree, Signal-to-Noise Ratio (SNR), sampling rate, and time slot length on TPI is explored. Finally, the proposed multi-node TPI method implemented using Graph Convolutional Network (GCN). Experimental results show that the GCN-based approach outperforms existing methods in both accuracy and efficiency. It achieves 0.978 accuracy at 0 dB and maintains the highest accuracy and precision under 40% data loss over a 5.7 s signal, demonstrating strong robustness. The accuracy degrades by less than 0.02 under multipath conditions. Moreover, on a 30-node network, it runs 0.04 s faster than the Transformer method with comparable accuracy.