Abstract
Unmanned aerial vehicle (UAV) has found promising applications in both military and civilian domains worldwide. In this article, we investigate the problem of distributed opportunistic spectrum access under the consideration of channel-slot selection simultaneously in multi-UAV networks from a game-theoretic perspective, and take into account the distinctive features of the multi-UAV network. We formulate the distributed joint channel-slot selection problem as a weighted interference minimization game. We prove that the formulated game is an exact potential game, and then use the distributed stochastic learning automata based joint channel and time slot selection algorithm to achieve the pure-strategy Nash equilibrium. The algorithm does not need information exchange among UAVs in the network which is more suitable for dynamic and practical enviroment. The simulation results demonstrate the effectiveness of the algorithm.
This work was supported in part by the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant BK20160034, in part by the National Science Foundation of China under Grant 61631020, Grant 61671473, and Grant 61401508, and in part by the Open Research Foundation of Science and Technology on Communication Networks Laboratory.
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Chen, J., Xu, Y., Zhang, Y., Wu, Q. (2018). Distributed Joint Channel-Slot Selection for Multi-UAV Networks: A Game-Theoretic Learning Approach. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-73447-7_59
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DOI: https://doi.org/10.1007/978-3-319-73447-7_59
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