基于多智能体深度强化学习的分布式协同干扰功率分配算法
Allocation Algorithm of Distributed Cooperative Jamming Power Based on Multi-Agent Deep Reinforcement Learning
查看参考文献31篇
|
文摘
|
针对战场通信对抗协同干扰中的干扰功率分配难题,本文基于多智能体深度强化学习设计了一种分布式协同干扰功率分配算法.具体地,将通信干扰功率分配问题构建为完全协作的多智能体任务,采用集中式训练、分布式决策的方式缓解多智能体系统环境非平稳、决策维度高的问题,减少智能体之间的通信开销,并加入最大策略熵准则控制各智能体的探索效率,以最大化累积干扰奖励和最大化干扰策略熵为优化目标,加速各智能体间协同策略的学习.仿真结果表明,所提出的分布式算法能有效解决高维协同干扰功率分配难题,相比于已有的集中式分配算法具有学习速度更快、波动性更小等优点,且相同条件下干扰效率可高出集中式算法16.8%. |
|
其他语种文摘
|
In order to solve the problem of jamming power allocation in battlefield cooperative communication countermeasures, this paper designs a distributed cooperative jamming power allocation method based on multi-agent deep reinforcement learning. Specifically, modeling the communication jamming power allocation as a fully cooperative multi-agent task, then the framework of centralized training and distributed decision-making is adopted to alleviate the characteristic of non-stationary environment and high dimensions in multi-agent system, reducing the communication overhead between agents as well, and introducing the maximum policy entropy criterion to control the exploration efficiency of each agent. Regarding maximizing the cumulative jamming reward and maximizing the entropy of the jamming policy as the optimization goal, then accelerates the learning of cooperative strategies. Simulation results indicate the proposed distributed method can effectively solve the high-dimensional cooperative jamming power allocation problem. Compared with the existing centralized allocation method, it has faster learning speed and less volatility, and the jamming efficiency is 16.8% higher than that of the centralized method under the same conditions. |
|
来源
|
电子学报
,2022,50(6):1319-1330 【核心库】
|
|
DOI
|
10.12263/DZXB.20210818
|
|
关键词
|
通信对抗
;
协同功率分配
;
多智能体深度强化学习
;
分布式策略
;
最大策略熵
|
|
地址
|
空军工程大学信息与导航学院, 陕西, 西安, 710077
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
0372-2112 |
|
学科
|
电子技术、通信技术 |
|
文献收藏号
|
CSCD:7240121
|
参考文献 共
31
共2页
|
|
1.
王沙飞. 认知电子战体系结构与技术.
中国科学:信息科学,2018,48(12):1603-1613,1709
|
CSCD被引
23
次
|
|
|
|
|
2.
Bayram S. Optimum power allocation for average power constrained jammers in the presence of non-Gaussian noise.
IEEE Communications Letters,2012,16(8):1153-1156
|
CSCD被引
4
次
|
|
|
|
|
3.
Xu C. Distributed subchannel allocation for interference mitigation in OFDMA femtocells: A utility-based learning approach.
IEEE Transactions on Vehicular Technology,2015,64(6):2463-2475
|
CSCD被引
5
次
|
|
|
|
|
4.
Gomadam K. Approaching the capacity of wireless networks through distributed interference alignment.
2008 IEEE Global Telecommunications Conference,2008:1-6
|
CSCD被引
3
次
|
|
|
|
|
5.
Amuru S. Jamming bandits-A novel learning method for optimal jamming.
IEEE Transactions on Wireless Communications,2016,15(4):2792-2808
|
CSCD被引
14
次
|
|
|
|
|
6.
颛孙少帅. 基于正强化学习和正交分解的干扰策略选择算法.
系统工程与电子技术,2018,40(3):518-525
|
CSCD被引
4
次
|
|
|
|
|
7.
Amuru S. Optimal jamming using delayed learning.
2014 IEEE Military Communications Conference,2014:1528-1533
|
CSCD被引
2
次
|
|
|
|
|
8.
黄志清. 基于深度强化学习的端到端无人驾驶决策.
电子学报,2020,48(9):1711-1719
|
CSCD被引
14
次
|
|
|
|
|
9.
Silver D. Mastering the game of Go with deep neural networks and tree search.
Nature,2016,529(7587):484-489
|
CSCD被引
758
次
|
|
|
|
|
10.
Vinyals O. Grandmaster level in StarCraft II using multi-agent reinforcement learning.
Nature,2019,575(7782):350-354
|
CSCD被引
162
次
|
|
|
|
|
11.
陈思光. 基于深度强化学习的云边协同计算迁移研究.
电子学报,2021,49(1):157-166
|
CSCD被引
7
次
|
|
|
|
|
12.
Li S. A Sample-Efficient Actor-Critic Algorithm for Recommendation Diversification.
Chinese Journal of Electronics,2020,29(1):89-96
|
CSCD被引
5
次
|
|
|
|
|
13.
杨启萌. 基于深度强化学习的维吾尔语人称代词指代消解.
电子学报,2020,48(6):1077-1083
|
CSCD被引
2
次
|
|
|
|
|
14.
Luong N C. Applications of deep reinforcement learning in communications and networking: A survey.
IEEE Communications Surveys & Tutorials,2019,21(4):3133-3174
|
CSCD被引
43
次
|
|
|
|
|
15.
Zhao D. A graph convolutional network-based deep reinforcement learning approach for resource allocation in a cognitive radio network.
Sensors(Basel, Switzerland),2020,20(18):5216-5239
|
CSCD被引
3
次
|
|
|
|
|
16.
Wang S X. Deep reinforcement learning for dynamic multichannel access in wireless networks.
IEEE Transactions on Cognitive Communications and Networking,2018,4(2):257-265
|
CSCD被引
6
次
|
|
|
|
|
17.
Xu Z Y. A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs.
2017 IEEE International Conference on Communications,2017:1-6
|
CSCD被引
1
次
|
|
|
|
|
18.
Guo D L. Joint optimization of handover control and power allocation based on multi-agent deep reinforcement learning.
IEEE Transactions on Vehicular Technology,2020,69(11):13124-13138
|
CSCD被引
3
次
|
|
|
|
|
19.
刘婷婷. 基于多智能体深度强化学习的分布式干扰协调.
通信学报,2020,41(7):38-48
|
CSCD被引
1
次
|
|
|
|
|
20.
Nasir Y S. Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks.
IEEE Journal on Selected Areas in Communications,2019,37(10):2239-2250
|
CSCD被引
25
次
|
|
|
|
|
了解气象卫星更多信息，请访问