Mode Selection in UAV-aided Vehicular Network: an Evolutionary Game Approach

Published on Oct 1, 2018
· DOI :10.1109/wcsp.2018.8555691
Guangchao Wang (THU: Tsinghua University), Sheng Zhou26
Estimated H-index: 26
(THU: Tsinghua University),
Zhisheng Niu32
Estimated H-index: 32
(THU: Tsinghua University)
In vehicular networks, safety-related applications require ultra high transmission reliability. An unmanned aerial vehicle (UAV) aided vehicular network is explored to enhance the reliability, where UAVs provide additional communication resources for vehicles. In this scenario, three communication modes are available for vehicles, i.e. vehicle to base station V2B, vehicle to vehicle V2V, and vehicle to UAV V2U. The transmission reliability highly depends on the selection of communication modes under different channel conditions and resource constraints, and thus the key issue is to appropriately select optimal communication modes to achieve the best reliability. In this paper, a mode selection approach based on evolutionary game is proposed in UAV-aided vehicular network, and an evolutionarily stable strategy is obtained. With the proposed algorithm, the proportion of vehicles in different communication modes can converge quickly to the evolutionary equilibrium where no vehicle has an incentive to change its communication mode. Numerical results show that our proposed algorithm exhibits a fast and controllable convergence and achieves higher transmission reliability with lower cost of resource utilization, compared with the selfish and random selection schemes.
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Mohammad Mahdi Azari6
Estimated H-index: 6
(Katholieke Universiteit Leuven),
Fernando Rosas11
Estimated H-index: 11
(Imperial College London)
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Estimated H-index: 24
(Katholieke Universiteit Leuven)
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Estimated H-index: 3
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Xiaomin Ma17
Estimated H-index: 17
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Estimated H-index: 11
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Estimated H-index: 2
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Ismail Guvenc34
Estimated H-index: 34
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Shi Yan7
Estimated H-index: 7
(Beijing University of Posts and Telecommunications),
Mugen Peng31
Estimated H-index: 31
(Beijing University of Posts and Telecommunications)
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Estimated H-index: 15
(VT: Virginia Tech),
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Estimated H-index: 43
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Estimated H-index: 9
(RyeU: Ryerson University),
Lian Zhao17
Estimated H-index: 17
(RyeU: Ryerson University)
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Estimated H-index: 41
(UW: University of Waterloo)
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Estimated H-index: 20
(UT: University of Twente),
Onur Altintas16
Estimated H-index: 16
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Estimated H-index: 5
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Martin Haenggi49
Estimated H-index: 49
(ND: University of Notre Dame),
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Estimated H-index: 23
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Dusit Niyato60
Estimated H-index: 60
(NTU: Nanyang Technological University),
Ekram Hossain66
Estimated H-index: 66
(UM: University of Manitoba)
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Ali Abdi29
Estimated H-index: 29
(UMN: University of Minnesota),
Mostafa Kaveh39
Estimated H-index: 39
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Estimated H-index: 2
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+ 5 AuthorsMingyu Gao (Hangzhou Dianzi University)
Abstract When a multi-UAV is performing a mission, the information obtained by the commander could be highly uncertain. How to choose strategies based on uncertain information will directly affect the success or failure of the UAV mission. In this paper, we present a multi-UAV confrontation model based on uncertain information, which can provide a powerful reference for strategy selection. We established a multi-UAV game model, and proposed a game payment function, and introduce a situation matr...
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