Coalition-based unmanned aerial vehicle (UAV) swarms havebeen widelyused in urgent missions. To fasten the completion, mobile edge computing (MEC) has been introduced into UAV networks where coalition leaders act as servers to help members with data computing. This paper investigates a relative delay optimization in MEC-assisted UAV swarms. Considering that the scheduling methods have great impact on the delay, some theoretical analyses are made and a scheduling method based on the shortest effective job first (SEJF) is proposed. Based on the coupled relationship between scheduling and resource allocation, the computation offloading and channel access problems are then jointly optimized. To solve the problem in distributed UAV networks, the optimization problem is formulated as an offloading game. It is proved that the game is an exact potential game (EPG) and it has at least one pure strategy Nash Equilibrium (PNE). To reach the PNE, a distributed offloading algorithm based on concurrent best-better response (CBBR) is designed. Finally, the simulations show that the performance of the proposed CBBR algorithm is better than traditional algorithms. Compared with other scheduling methods, the proposed scheduling method based on SEJF reduces the delay by up to 30%.

Capable of delivering contents offloaded from the base station (BS) to users, unmanned aerial vehicle (UAV) has emerged as a crucial leverage to compensate for terrestrial BSs-based communication. However, the limited storage capacity of the UAV brings challenges to providing low-latency services for users. In this paper, we investigate the caching placement of the UAV for enhancing the timeliness of services. To overcome the unknown content popularity, proximal policy optimization (PPO) is adopted in the proposed algorithm. To be specific, we first propose a modified timeliness model, named effective age of information (EAoI), to comprehensively evaluate the timeliness of services. Then, we employ PPO to build a deep reinforcement learning framework for finding the optimal caching strategy adaptively. Extensive simulation results are provided to verify the superiority of the proposed scheme, in comparison with the conventional schemes.

The communication relationship can reflect the behavior relationship between different communication targets. The in-depth analysis of the communication relationship can obtain the behaviors of communication individuals, and speculate their hierarchical positions in the communication network, so as to provide a basis for further speculation on the structure of the communication network. For massive spectrum signals, we can also obtain important information such as communication relationships and behaviors of communication individuals, without cracking the signal content, but by analyzing the physical characteristics and statistical laws of the spectrum signals. In order to overcome the difficulties and costs of analyzing communication behaviors from cracking the signal content in existing research, this paper studies the physical characteristics and statistical laws of spectrum signals based on the features of frequency hopping period, average power and time of signal occurrence. Because the spectrum signals generated by the communication individuals show clustering characteristics, this paper proposes a communication relationship mining method based on improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The method can accurately discover the communication relationship of the radio station from the incomplete spectrum monitoring data, without cracking the content carried by the spectrum signals, which provides a new idea for the mining and analysis of mass spectrum monitoring signals.

This paper investigates the anti-jamming problems in wireless communication networks. In these networks consisted of multiple devices (users), there exist two critical problems. On the one hand, users with various transmission requirements should coordinate their channel selection strategies distributedly to avoid spectrum conflicts and satisfy transmission demands. On the other hand, they also need to fully consider how to eliminate the effects of malicious attacks. To cope with the internal coordination and external confrontation challenges and accommodate the dynamic changing jamming attacks, a context-aware distributed coordinated anti-jamming channel access mechanism is proposed, which means for different cases of jamming attacks, different access strategies are adopted. In detail, to reflect the heterogeneous communication demands of users, the transmission satisfaction function is firstly introduced. Then, the multi-user anti-jamming scenario is modeled as a context-aware multi-pattern dynamic anti-jamming game, which can be decomposed into two sub-games. Here, for the case that the control channel is available, a local altruistic sub-game is introduced. While for the case that the control channel has been jammed, an anti-jamming congestion sub-game is designed. Besides, the existence of Nash Equilibriums is demonstrated. To obtain NEs, a context-aware distributed channel access (CDCA) algorithm is designed. Through game-theoretic analysis and distributed learning, global transmission satisfaction can be improved under the dynamic jamming environment. Furthermore, the fairness of the network can also be guaranteed.

Radio frequency (RF)-based drone identification is a safety-critical task, where erroneous model outputs may result in potential costs. However, most existing studies are based on the closed-set assumption, with only a few studies considering the presence of out-of-distribution (OOD) samples during testing. This paper argues that a reliable model should strive to minimize errors in classification, necessitating the ability to detect both OOD and misclassified ID samples (IDꭗ), as both types can lead to model misclassifications. We term this task Reliable Drone RF Signal Identification (RDI). To address the RDI task, this paper proposes an extreme value theory (EVT)-based method for simultaneously detecting these two types of samples. Initially, we propose employing a Generalized Pareto Distribution (GPD) model and establishing an uncertainty scoring function for each ID class, based on correctly identified in-distribution samples (ID). The proposed function effectively assigns lower uncertainty to ID and higher uncertainty to IDꭗ and OOD samples. Moreover, we tackle the GPD threshold selection issue, given its paramount significance in the construction of the GPD model. This paper proposes a GPD threshold selection algorithm based on minimized Kullback-Leibler (KL) divergence (MKL-based GPDTS). MKL-based GPDTS computes an appropriate GPD threshold for each ID class, based on the information of that class. We conduct comprehensive validation of the proposed approach using a self-collected drone RF signal dataset and an open-source dataset. The experimental results demonstrate the effectiveness of the proposed method.

Regulation of drones is already an important research topic. The drone identification method based on the radio frequency (RF) signal analysis technology is an efficient approach to regulate mainstream civilian drones. However, existing research on drone identification has been conducted under high signal-to-noise ratio (SNR) conditions. In fact, long-range drone identification in urban environments is performed under low SNR conditions. In this article, a global context (GC)-based threshold strategy is proposed to solve the above problem. The threshold is automatically determined during the deep architecture optimization. Meanwhile, the unimportant features in the network are removed by threshold denoising. First, the weight of each feature channel is obtained by modeling cross-channel dependencies, and then a weighted average is performed for each position along the feature channel on the feature map to obtain the thresholds. The method reduces the impact of unimportant features on performance without complex data preprocessing. In the experiments, the performance and complexity of the proposed method are extensively evaluated by 11 different drone RF signals. The experimental results show that in comparison with other deep learning-based threshold calculation methods, the proposed method has a more outstanding performance and lower computational complexity for drone RF signal identification under the low SNR condition.