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您的检索式：作者名="Yinxiang Qu"
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| 1 | Enhancing source camera identification performance with a camerareference phase sensor pattern noise 显示文摘 | Kang Xiangui Li Yinxiang Qu Zhenhua | 2012 | IEEE Transac- tions on Information Forensics and Security2012，7，2： | 1 |
| 2 | Enhancing source camera identification performance with a camera ref- erence phase sensor pattern noise 显示文摘 | Kang Xiangui Li Yinxiang Qu Zhenhua | 2012 | IEEE Transactions on Information Forensics and Security2012，7，2： | 1 |
| 3 | Enhanc- ing source camera identification performance with a camera reference phase sensor pattern noise 显示文摘 | Kang Xiangui Li Yinxiang Qu Zhenhua | 2012 | IEEE Transactions on Information Forensics and Security2012，7，2： | 1 |
| 4 | Resource Allocation and Power Control Policy for Device-to-Device Communication Using Multi-Agent Reinforcement Learning显示文摘Device-to-Device(D2D)communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity.In this paper,we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput.Firstly,we treat each D2D pair as an independent agent.Each agent makes decisions based on the local channel states information observed by itself.The multi-agent Reinforcement Learning(RL)algorithm is proposed for our multi-user system.We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected,so the problem is modeled as a stochastic non-cooperative game.Hence,each agent becomes a player and they make decisions together to achieve global optimization.Thereby,the multi-agent Q-learning algorithm based on game theory is established.Secondly,in order to accelerate the convergence rate of multi-agent Q-learning,we consider a power allocation strategy based on Fuzzy C-means(FCM)algorithm.The strategy firstly groups the D2D users by FCM,and treats each group as an agent,and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users.The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system.In particular,FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput. | Yifei Wei Yinxiang Qu Min Zhao Lianping Zhang F.Richard Yu | 2020 | Computers, Materials & Continua2020，，6： | 0 |
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