Coalition structure is an efficient networking architecture for task implementation in unmanned aerial vehicle (UAV) networks. However, both the formation of coalition and the spectrum resource for intra-coalition communication affect the reconnaissance performance. In this paper, we investigate a cooperative reconnaissance and spectrum access (CRSA) scheme for task-driven heterogeneous coalition-based UAV networks by jointly optimizing task layer and resource layer. Specifically, coalition formation game (CFG) is formulated to jointly optimize task selection and bandwidth allocation. In addition to the traditional Pareto order and selfish order, coalition expected altruistic order maximizing coalitions' utility is proposed. The CFG under the proposed order is proved to be an exact potential game (EPG). Then the existence of stable coalition partition is guaranteed with the help of Nash equilibrium (NE). We propose a joint bandwidth allocation and coalition formation (JBACF) algorithm to achieve stable coalition partition wherein an efficient gradient projection (GP) based method is applied to solve bandwidth allocation. The effectiveness of the proposed scheme and algorithms are demonstrated through in-depth numerical simulations. The results show that our proposed CRSA scheme is superior to non-joint optimization scheme. Also, the proposed order is superior to traditional Pareto order and selfish order.

To tackle the multi-user and multi-channel issues in anti-jamming problems, we model the problem as a multi-leader one-follow Stackelberg game. An anti-jamming mechanism with “No Pains No Gains” idea is designed. The proposed mechanism sacrifices parts of users' benefits to trap the jammer, while the rest of users can achieve a better performance. In this way, the system's throughput is improved. In addition, the channel bonding technology is used to enhance the robust performance and improve system throughput further. Besides the cooperative anti-jamming problem is modeled as a multi-leader one-follower Stackelberg game, the channel selection and power allocation game of leaders is also modeled as a potential game. The existence of Nash equilibrium is proved, which guarantees the Stackelberg equilibrium of users and the jammer. A joint channel selection and power allocation algorithm is modified to approach the final equilibrium. The simulation results show that the proposed anti-jamming mechanism with channel bonding achieves higher throughput and better anti-jamming performance than non-cooperative algorithms.

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.

Over the past decades, the unmanned aerial vehicle (UAV) has received unprecedented surge of scientific and military interest worldwide. This article investigates the problem of opportunistic spectrum access for multi-UAV networks from a game-theoretic perspective. Due to the topology of the multi- UAV networks, the interference may be classified into two parts, i.e., the intra-cluster and the inter-cluster interference. Moreover, since the UAVs in the network have different tasks, the communication demand of each UAV should be taken into account. First, we formulate the demand-aware joint channelslot selection problem as a weighted interference mitigation game, and then design the utility function considering features of multi-UAV network, e.g., some rewards due to the channel and slots selection. We prove that the formulated game is an exact potential game with at least one pure-strategy Nash equilibrium (NE). Next, we apply the distributed log-linear algorithm to achieve the desired optimization and overcome the constraint of dynamic communication demand of each UAV. To speed up the convergence, we also propose a low-complexity and realistic channel and slot initialization scheme for UAVs. Finally, the simulation results validate the effectiveness of the formulated game.