Dynamic Anti-Jamming Coalition for Satellite-Enabled Army IoT: A Distributed Game Approach

被引:54
作者
Han, Chen [1 ]
Liu, Aijun [1 ]
Wang, Haichao [1 ]
Huo, Liangyu [2 ]
Liang, Xiaohu [3 ,4 ]
机构
[1] Army Engn Univ PLA, Coll Commun Engn, Nanjing 210007, Peoples R China
[2] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
[3] Southeast Univ, Sch Informat Sci & Engn, Nanjing 210018, Peoples R China
[4] Army Engn Univ PLA, Coll Commun Engn, Nanjing 210007, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Anti-jamming; coalition formation game (CFG); Internet of Things (IoT); potential game; Q-learning; Stackelberg game; COMMUNICATION; INTERNET; UAV; FRAMEWORK; MODEL;
D O I
10.1109/JIOT.2020.2991585
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Satellite-enabled army Internet of Things (SaIoT) has drawn increasing attention due to the wide-coverage and large-capacity transmission. However, the smart jamming based on artificial intelligence technologies has seriously degraded SaIoT performance. Thus, this article investigates the distributed dynamic anti-jamming scheme for SaIoT to decrease energy consumption in the jamming environment. First, a hierarchical anti-jamming Stackelberg game (HASG), which consists of the leader subgame for jammers and the follower subgame for SaIoT devices, is proposed to formulate the confrontation interaction between jammers and SaIoT devices. It has been proved that there exists a Stackelberg equilibrium in the proposed HASG. Then, an anti-jamming coalition formation game (CFG) is proposed for the follower subgame to decrease the energy consumption in the jamming environment, and the modified coalition preference order and coalition change principle are put forward to enhance the performance of the proposed anti-jamming CFG. Furthermore, with the help of the exact potential game, we have demonstrated that the proposed anti-jamming CFG could converge to the stable coalition formation and it is able to achieve similar performance to the centralized optimization via a distributed approach. Finally, reinforcement-learning-based algorithms are utilized to obtain the suboptimal anti-jamming policies according to the dynamic and unknown jamming environment, and simulation results validate that the proposed approach achieves better performance than the existing approaches.
引用
收藏
页码:10932 / 10944
页数:13
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