In this paper, we study the distributed relay assignment problem in multi-channel multi-radio unmanned aerial vehicle (UAV) communication networks. Multi-UAVs are driven by the overall task and fly in certain formation, where drones with different tasks have various transmission requirements. Source UAVs equipped with multi-radio can select more than one relay radios to achieve high data rate, and each relay radio can be shared by multiple source UAVs. We construct distributed game models to promote the global transmission performance by self-organizing coordination among UAVs. Specifically, the channel competition relationship between relay UAVs is modeled as a congestion game model, while the task-driven relay selection among UAVs is modeled as a many-to-many matching market without substitutability. With the proposed game models, the optimizing of local optimized process will lead to the improvement of global transmission results. After that, we design algorithms for the stable and changeable topology structures, respectively. Based on the given formation shape of UAVs, a learning matching algorithm is proposed to reach the optimum result with a large probability. A fast potential matching algorithm is propose to deal with the topological change of UAV networks. We prove that two proposed algorithms can both achieve the stable matching results. Simulation results show that the proposed relay assignment approaches yield good performances in terms of the global transmission satisfaction and fairness. Particularly, the result of the learning algorithm is close to the global optimum and the fast potential matching approach is robust to the perturbation of UAV networks.

In this paper, we study the multi-task cooperation problem for unmanned aerial vehicle (UAV) swarms, where the UAV energy consumption is taken into consideration during location scheduling and task implementation. One task may need the cooperation of several UAVs with specific capabilities. To avoid unreasonable task allocation, we quantify the mission properties of UAVs and task areas. We comprehensively consider the overlapping and complementary relationship of the UAV’s task types, so that UAVs can form corresponding collective execution tasks according to the task attributes. Based on the coalition game theory, we model the distributed task assignment problem of UAVs as a coalition formation game (CFG). We propose a task allocation algorithm, and then prove that it can achieve the joint optimization of energy and task completion by decision-making of UAVs in finite iterations. With the equilibrium properties of coalition formation in UAV networks, we further optimize the position of UAVs to minimize the network energy consumption. Simulation results verify that the proposed method can reduce the flight loss with high task completion degree.

In this paper, unmanned aerial vehicle (UAV) and reconfigurable intelligent surfaces (RIS) are combined together to further enhance the covert communication system. In particular, a multi-antenna transmitter Alice transmits information to Bob, through UAV-RIS in the presence of an adversarial warden, whose channel state information (CSI) is not perfectly known at Alice. To simultaneously guarantee the covertness and the robustness, the minimum worst-case average covert transmission rate is maximized by jointly optimizing the beamforming vector, the phase shift vector and UAV trajectory. Moreover, the robust design of beamforming, phase shift and three-dimensional (3D) trajectory is formulated as a non-convex problem. To deal with the intractable optimization problem, an alternative optimization algorithm consisting of three subproblems is designed to deal with the joint optimization problem. The general sign-definiteness is utilized to transform the CSI uncertainty and successive convex approximation (SCA) methods are proposed to transform non-convex constraints. Besides, considering the high complexity of the proposed SCA based algorithm, we further develop a low-complexity algorithm for a special case of perfect CSI scenario, where analytical expressions of the beamforming and phase shift are derived. Finally, simulation results demonstrate that the noise uncertainty for hiding legitimate communication is not necessarily as high as possible for a large equivalent receiving power threshold β and the proposed scheme outperforms the state-of-the-art schemes.

In this paper, we proposed a context-aware group buying mechanism to reduce users' data cost based on the content similarity. Each user's cost is formulated as the combination of the content-aware data cost and location-aware sharing cost. Data cost is the payoff of the spectrum owner's channel to download files and sharing cost is the energy and time cost in transmitting files among the coalition. Compared with downloading data alone, users would like to form different groups and download the traffic data first and then share data among the group, to achieve a lower cost. The cost reducing problem through group buying mechanism is modeled as a coalition formation game (CFG). Besides the traditional Pareto order, a coalition order maximizing the coalition's benefit, and a selfish order maximizing users' benefit are proposed. The CFGs with the two proposed orders are proved to be potential games respectively, and the existence of the stable coalition partitions are also guaranteed by Nash equilibriums. A cooperative exchange mechanism is designed, where users can make decisions cooperatively to achieve better performance. Simulation results show that the context awareness group buying reduces the cost and improves the benefit significantly compared with the situation without context awareness. The proposed orders both have better performance than the Pareto order.

In this paper, we study the unmanned aerial vehicle (UAV)-enabled opportunistic data collection in wireless sensor networks (WSNs). The UAV performing remote missions is expected to collect data from the WSN during the return flights. Due to the specified task and safety restrictions, flight trajectory and time of the UAV are strictly constrained, resulting in the limited coverage ability in the data collection process. Moreover, the unknown distribution of active sensors makes it difficult for ground sensors and the UAV to complete the off-line optimization of flight mode and transmission. To tackle these problems, we develop an air-ground collaborative online planning method. On one hand, ground sensors actively form terrestrial transmission clusters to improve the data upload efficiency. After analyzing the line-of-sight (LoS) reliability and transmission correlation, we construct a coalition formation game model for the clustering of ground sensors. We discuss the equilibrium property of the game model, which can be achieved by the proposed distributed coalition formation algorithm. On the other hand, to avoid conflicts during the data collection, a data upload protocol is designed. We further discuss various flight speed planning schemes based on different detection capabilities of the UAV. The simulation results show that the performance of ground coalition-based air-ground collaborative online optimization is much better than that of the unilateral data collection by the UAV. Moreover, UAV flight online planning can further improve data uploading efficiency.

In this letter, the frequency selection problem in jamming environment with large number of optional frequencies is investigated. With numerous optional actions in the wider frequency band scenario, most of existing anti-jamming methods will become ineffective, since the convergence time and computational complexity will grow exponentially with the number of actions. To cope with the above challenge, a novel hierarchical deep reinforcement learning algorithm which does not need to know the jamming patterns and channel model is proposed. The proposed algorithm divides the frequency selection problem in the broadband into two steps via two subnetworks: Firstly, the frequency band is selected by the band selection network, and then the specific frequency is selected in this frequency band by the frequency selection network. Simulation results show that the proposed algorithm avoids multiple different jammings effectively and achieves satisfactory throughput with less calculation.