UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games (EPG) and have Nash equilibrium (NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play (MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.

Unmanned aerial vehicle (UAV) swarms require accurate relative localization to safeguard flight missions in the global navigation satellite system-denied environment due to a lack of absolute position information. The existing work of relative localization faces challenges, such as the ranging information loss and the low localization frequency caused by long-distance ranging and large-scale characteristic of the UAV swarm. This article proposes a clustering-based cooperative relative localization scheme for UAV swarm, which contains a two-level framework: inter/intra-cluster localization. In order to investigate the tradeoff between intracluster cooperation and intercluster packet loss, the clustering-based problem is constructed as a coalition formation game (CFG) model. Given the designed coalition value, preference relation, and the coalition formation principles, it is proved that the proposed CFG model has a Nash stable partition. The designed coalition formation algorithm includes coalition heads and beacon drones selection mechanism. Simulation results show that the proposed CFG algorithms shorten the ranging time compared with global localization and achieve better localization performance (localization error and success rate) than contrast algorithms.

This paper mainly investigates the multi-user anti-jamming spectrum access problem. Using the idea of "converting harm into benefit", the malicious jamming signals projected by the enemy are utilized by the users as the coordination signals to guide spectrum coordination. An "internal coordination-external confrontation" multi-user anti-jamming access game model is constructed, and the existence of Nash equilibrium (NE) as well as correlated equilibrium (CE) is demonstrated. A coordination-learning based anti-jamming spectrum access algorithm (CLASA) is designed to achieve the CE of the game. Simulation results show the convergence and effectiveness of the proposed CLASA algorithm, and indicate that our approach can help users confront the malicious jammer and coordinate internal spectrum access simultaneously without information exchange. Last but not least, the fairness of the proposed approach under different jamming attack patterns is analyzed, which illustrates that this approach provides fair anti-jamming spectrum access opportunities under complicated jamming pattern.

In a multi-UAV system, some collected data should be transmitted to relevant UAVs or ground station for processing when energy resources are insufficient or computation consumption is large. In this paper, we researched the energy-saving strategies for data aggregation on the combination of store-carry-forward (SCF) routing and hop-by-hop routing, which are suitable for large-scale multi-UAV networks, and proposed Single Coalition Strategy (SCS) and Coalition Formation Strategy (CFS) based on the coalition game theory. In SCS, all source UAVs transmit data individually or can form a coalition. The coalition aggregates all data to a ferry UAV for transmission, the ferry choose between SCF routing mode and hop-by-hop routing mode with the help of predicted energy-cost calculation. In CFS, all source UAVs form a coalition structure by coalition formation game (CFG) algorithm according to data amount, location, and network topology. Each coalition can aggregate data to its own ferry, and then the ferry transmits data in the optimized mode. We analyze the performance of SCS and CFS by simulations, the average transmission energy consumption of a UAV in SCS and CFS are about 30% of hop-by-hop routing mode. Based on the results, we recommend that SCS should be used when the application scenario is small and the number of UAVs is limited. In the case where the application scenario is great and the number of UAVs is large, CFS with the Utilitarian order and limited message propagation hops is recommended.

Unmanned aerial vehicle (UAV) cooperative control has been an important issue in UAV-assisted sensor networks, thanks to the considerable benefit obtained from the cooperative mechanism of UAVs being applied as a flying base station. In a coverage scenarios, the trade-off between coverage and transmission performance often makes deployment of UAVs fall into a dilemma, since both indexes are related to the distance between UAVs. To address this issue, UAV coverage and data transmission mechanism is analyzed in this paper; then, an efficient multi-UAV cooperative deployment model is proposed. The problem is modeled as a coalition formation game (CFG). The CFG with Pareto order is proved to have a stable partition. Then, an effective approach consisting of coverage deployment and coalition selection is designed, wherein UAVs can decide strategies cooperatively to achieve better coverage performance. Combining analysis of game approach, coalition selection and the position deployment algorithm based on Pareto order (CSPDA-PO) is designed to execute coverage deployment and coalition selection. Finally, simulation results are shown to validate the proposed approach based on an efficient multi-UAV cooperative deployment model.

The unmanned swarm system (USS) has been seen as a promising technology, and will play an extremely important role in both the military and civilian fields such as military strikes, disaster relief and transportation business. As the “nerve center” of USS, the unmanned swarm communication system (USCS) provides the necessary information transmission medium so as to ensure the system stability and mission implementation. However, challenges caused by multiple tasks, distributed collaboration, high dynamics, ultra-dense and jamming threat make it hard for USCS to manage limited spectrum resources. To tackle with such problems, the machine learning (ML) empowered intelligent spectrum management technique is introduced in this paper. First, based on the challenges of the spectrum resource management in USCS, the requirement of spectrum sharing is analyzed from the perspective of spectrum collaboration and spectrum confrontation. We found that suitable multi-agent collaborative decision making is promising to realize effective spectrum sharing in both two perspectives. Therefore, a multi-agent learning framework is proposed which contains mobile-computing-assisted and distributed structures. Based on the framework, we provide case studies. Finally, future research directions are discussed.