| 基于Q-Learning的深度神经网络自适应退避策略 |
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| 作者姓名： | 毛中杰 俞 晖 麻智超 王 政 |
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| 作者单位： | 上海交通大学;北京遥测技术研究所 |
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| 基金项目： | 国防基础科研计划“十三五”项目(NO.JCKY2017203B082)。 |
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| 摘 要： | 针对无人机自组织网络,结合Q-Learning和深度神经网络,提出一种自适应退避策略,以提高基于竞争的MAC协议通信性能。以Matlab为仿真平台,仿真比较了自适应退避策略与二进制指数退避策略的性能。
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| 关 键 词： | 无人机 Q-LEARNING 深度神经网络 退避策略 |
An adaptive back-off strategy based on deep Q-Learning neural network |
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| Authors: | MAO Zhongjie YU Hui MA Zhichao WANG Zheng |
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| Institution: | (Shanghai Jiao Tong University,Shanghai 200240,China;Beijing Research Institute of Telemetry,Beijing 100076,China) |
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| Abstract: | An adaptive back-off strategy based on Q-Learning and deep neural network is proposed to improve the communication performance of MAC protocol based on competition for unmanned aerial vehicle self-organizing network.In the experiment,Matlab is used as the simulation platform to compare the performance of adaptive back-off strategy and binary exponential back-off strategy. |
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| Keywords: | Unmanned aerial vehicle Q-Learning Deep neural network Back-off strategy |
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