Interference-Aware Online Distributed Channel Selection for Multicluster FANET: A Potential Game Approach

Chen, Jiaxin; Xu, Yuhua; Wu, Qihui; Zhang, Yuli; Chen, Xueqiang; Qi, Nan

doi:10.1109/tvt.2019.2902177

Cited by 72 publications

(39 citation statements)

References 44 publications

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“…Multi-cluster flying ad hoc networks (FANET) under different network topologies have interference-aware online spectrum access problems. The online distributed algorithm based on the optimal response (IOCPCBR) algorithm can effectively reduce the interference of the UAV cluster and reduce the cost of channel switching per time slot [108]. Albu-Salih AT and others proposed a new framework to improve the data collection efficiency of wireless sensor networks and multiple drones, and minimize the total travel distance, travel time and network energy consumption of drones.…”

Section: Dql Strategymentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

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Section: Dql Strategymentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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“…In [19], the authors proposed an anti-coordination game based scheme to solve the dynamic spectrum sharing problem in the scenario where UAVs and D2D users can share the same spectrum. In [20], the authors proposed a potential game based online channel selection scheme for multicluster Flying Ad-Hoc Network (FANET). However, the computational complexity of the proposed schemes in both papers is too high in dynamic and stochastic environment, since each user has to be traversed more than once when the state changes.…”

Section: A Related Workmentioning

confidence: 99%

UAV-Assisted Emergency Communications in Social IoT: A Dynamic Hypergraph Coloring Approach

Wang

Sun

et al. 2020

IEEE Internet Things J.

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In this paper, we address social-awareness property and unmanned aerial vehicle (UAV) assisted information diffusion in emergency scenarios, where the UAVs can disseminate alert messages to a set of terrestrial users within their coverage, and then these users can continuously disseminate the received data packets to their socially connected users in a device-to-device (D2D) multicast manner. In this regard, we have to solve both the dynamic cluster formation and spectrum sharing problems in stochastic environments, since both the UAVs and terrestrial users may arrive or depart suddenly. For the cluster formation problem, considering that the data rate of a multicast cluster is determined by the member with worst link condition, we formulate it as a many-to-one matching game and adopt the rotation-swap algorithm to maximize the expected number of users receiving the alerting messages in each time slot. For the dynamic spectrum sharing problem, aiming at eliminating the interference while minimizing the channel switching cost, we propose a dynamic hypergraph coloring approach to model the cumulative interference and maintain the mutual interference at a low level by exploring a small number of vertexes, when the graph is dynamically updated, i.e., the insertion/deletion of vertex/edge. Moreover, we prove some crucial properties including global stability, convergence, and complexity. Finally, simulation results shows that our proposed approach can achieve a better trade-off among the information diffusion speed, channel switch cost, and complexity.

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“…Some research investigated distributed spectrum resource sharing in USCS [15]- [18]. All of these works aimed to realize spectrum collaboration for multi-UAV networks in a distributed manner.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions

Wang

Chen

et al. 2020

IEEE Access

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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. INDEX TERMS Unmanned swarm system, spectrum sharing, machine learning, multi-agent learning, game theory.

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Interference-Aware Online Distributed Channel Selection for Multicluster FANET: A Potential Game Approach

Cited by 72 publications

References 44 publications

UAV Swarm Intelligence: Recent Advances and Future Trends

UAV Swarm Intelligence: Recent Advances and Future Trends

UAV-Assisted Emergency Communications in Social IoT: A Dynamic Hypergraph Coloring Approach

Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions

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