Packet routing is a fundamental problem in wireless networks in which routers decide the next hop for each packet in order to deliver it to its destination as quickly as possible. In order to overcome the shortcoming of non optimal forwarding path caused by fixed forwarding mode in geographic location-based routing algorithm, we investigated a new efficient packet routing strategy which combined with Deep Reinforcement Learning (DRL) algorithm (Proximal Policy Optimization: PPO) to minimize the hops and reduce the probability of encountering “routing hole” during the forwarding of the packets in complex networks. Each node in our network can make routing decisions to learn a routing policy that can not only use the greedy mode to maintain the efficiency of routing policy but also reduce the use of peripheral forwarding mode. The learning process of the DRL agent is based on the status information of the packet being transmitted and information of neighbor nodes within the communication range. Extensive simulations over dynamic network topologies and the number of nodes have shown that our packets routing agent can learn the optimal policy in terms of average packets delivery rate and the number of hops compared with Greedy Perimeter Stateless Routing (GPSR) protocol. The performance of PPO in large-scale action space is also verified, which provides a basis for the future researches to combine PPO technique with packet routing optimization.

Dragonfly algorithm (DA) is a recently proposed optimization algorithm based on swarm intelligence, which has been successfully applied in function optimization, feature selection, parameter adjustment, etc. However, it fails to take individual optimal position into consideration but only relies on population optimal position and 5 behaviours to update individual position, leading to low accuracy, slow convergence, and local optima. To overcome these drawbacks, Tent Chaotic Map and Population Classification Evolution Strategy-Based Dragonfly Algorithm (TPDA) is proposed. Tent chaotic map is used to initialize the population, making individuals distributed more uniformly in search space to improve population diversity and search efficiency. Population is classified according to individual fitness value, and different position update methods are adopted for different types of individuals to guide the search process and improve the ability of TPDA to jump out of local optima, thus realizing a balance between exploration and exploitation. The efficiency of TPDA has been validated by tests on 18 basic unconstrained benchmark functions. A comparative performance analysis between TPDA, Particle Swarm Optimization (PSO), DA, and Adaptive Learning Factor and Differential Evolution-Based Dragonfly Algorithm (ADDA) has been carried out. Experimental and statistical results demonstrate that TPDA gives significantly better performances compared with PSO, DA, and ADDA on the average and standard deviation in all 18 functions. The global optimization capability of TPDA on high-dimensional functions and the comparison of the time complexity of TPDA and other swarm intelligence algorithms is also verified in the paper. The results indicate that TPDA is able to perform better on optimizing functions without consuming more computational time.

Aiming at anti Unmanned Aerial Vehicle (UAV) swarm, this paper studies the detection and suppression mechanisms of emergence in cooperative flight. Cooperative fly is one of the critical operations for UAV swarm in both military and civilian utilities, which allows individual UAVs to distributed adjust their velocity to head for a common destination as well as avoid a collision. This process is viewed as the emergence of complex systems. An emergence detection algorithm based on double thresholds is proposed. It simultaneously monitors the cooperative flight process and system connectivity to accurately identify the occurrence, achievement, or failure of cooperative fly, which provides a solid prerequisite for the suppression mechanism. For suppression, in-band radio interference is designed under the constraint of average power, and the effect is modeled from the perspective of degrading the communication performance of the target system. It is found that low-intensity continuous interference can effectively delay the cooperative fly process and has better concealment, while medium-intensity continuous interference can rapidly stop that process. Based on the above analysis, for the first time, two countermeasures for the UAV swarm’s cooperative fly are designed for the operation intent of delay and disruption of the target UAC swarms, respectively. Simulation results show the effectiveness of the countermeasures.

The unmanned aerial vehicle (UAV) has drawn attention from the military and researchers worldwide, which has advantages such as robust survivability and execution ability. Mobility models are usually used to describe the movement of nodes in drone networks. Different mobility models have been proposed for different application scenarios; currently, there is no unified mobility model that can be adapted to all scenarios. The mobility of nodes is an essential characteristic of mobile ad hoc networks (MANETs), and the motion state of nodes significantly impacts the network’s performance. Currently, most related studies focus on the establishment of mathematical models that describe the motion and connectivity characteristics of the mobility models with limited universality. In this study, we use a backpropagation neural network (BPNN) to explore the relationship between the motion characteristics of mobile nodes and the performance of routing protocols. The neural network is trained by extracting five indicators that describe the relationship between nodes and the global features of nodes. Our model shows good performance and accuracy of classification on new datasets with different motion features, verifying the correctness of the proposed idea, which can help the selection of mobility models and routing protocols in different application scenarios having the ability to avoid repeated experiments to obtain relevant network performance. This will help in the selection of mobility models for drone networks and the setting and optimization of routing protocols in future practical application scenarios.

Link prediction is a topic within the realm of graph theory. However, the majority of existing link prediction models are tailored for static graphs, disregarding the temporal evolution of graphs and the significance of global features during this process. Addressing the high dynamics and time‐varying feature of unmanned aerial vehicle (UAV) network, traditional static graph methods encounter issues with the loss of network feature extraction information. By slicing the motion process of UAV nodes to form dynamic sequence graphs and designing sub‐modules of structural attention and temporal attention, we characterize the spatial relationships within subgraphs of dynamic sequence graphs and the temporal relationships between subgraphs. We propose a dynamic link prediction method using dynamic graph neural networks, which possesses exchangeability and interpretability, capturing the temporal dimension features of the entire evolution process of the cluster dynamic network and characterizing the spatial relationships between nodes. A dataset of time‐varying topologies for cluster networks is constructed, enabling link prediction under complex dynamic networks with time‐varying conditions. Experimental results demonstrate that this method can accurately predict link relationships between nodes in time‐varying complex UAV network topologies. Compared with traditional dynamic graph network models such as node2vec and GraphSAGE, this model achieves a link prediction accuracy of 88.79% on the RPGM dataset, surpassing node2vec's 70.24% and GraphSAGE's 62.81%.

As a critical component of unmanned aerial vehicle (UAV) data link systems, networking radios used to create wireless ad‐hoc networks demonstrate considerable susceptibility to ultra‐wideband ultra‐wideband electromagnetic pulse (UWB‐EMP) irradiation, resulting in severe degradation of communication performance. This paper presents a systematic investigation based on irradiation experiments, which clearly reveal front‐door interference effects in networking radios exposed to UWB‐EMP. To elucidate the underlying interference mechanisms, a QPSK communication system model and an RF module circuit model were developed. These models indicate that the observed blocking interference is primarily attributable to non‐linear effects within the RF front‐end. Throughout the study, the BER was employed as the evaluation metric for both irradiation tests and QPSK system simulations, while the output waveform of the low‐noise amplifier served as the key indicator in circuit‐level simulations. Experimental results indicate that UWB‐EMP irradiation induces communication blackouts, with a data transmission success rate of only 66.7% within the specified duration. The antenna effectively couples UWB‐EMP energy into the communication system, causing nonlinear distortion in the RF module. This distortion significantly increases the BER, reaching up to 50% under the given simulation conditions and may even lead to complete system failure. Detailed analysis shows that: (1) at 5 kV irradiation, the LNA enters nonlinear operation, losing amplification capability for 3–7 ns before recovery; (2) at 10 kV, transistor damage becomes more severe, extending the LNA's dysfunction period to 3–30 ns; and (3) at 30 kV and 60 kV, complete transistor breakdown occurs, with recovery times prolonged to 70 ns and 105 ns, respectively. These findings provide essential theoretical and experimental foundations for enhancing the electromagnetic compatibility and protection of networking radios in ultra‐wideband electromagnetic pulse (EMP) environments.