文章摘要
唐一强, 杨霄鹏, 肖楠, 朱圣铭.基于深度强化学习的卫星信道动态分配算法[J].空军工程大学学报:自然科学版,2022,23(2):61-67
基于深度强化学习的卫星信道动态分配算法
A Dynamic Allocation Algorithm of Satellite Channels Based on Deep Reinforcement Learning
  
DOI:
中文关键词: 卫星通信  深度学习  Q算法
英文关键词: satellite communication  deep learning  Qlearning algorithm
基金项目:国家自然科学基金(61871474)
作者单位
唐一强, 杨霄鹏, 肖楠, 朱圣铭 空军工程大学信息与导航学院 西安 710077 
摘要点击次数: 61
全文下载次数: 42
中文摘要:
      在卫星通信系统中,频率和信道是十分珍稀的资源,针对如何利用可靠且高效的方法来进行资源的开发这一亟需解决的难题,提出了一种基于Q-learning深度强化学习的动态卫星信道分配算法DRL-DCA,该算法将卫星和环境交互建模为马尔科夫决策过程,通过环境的反馈提升卫星的决策能力,实现用户业务请求的高效应答,提升卫星通信的服务质量,降低通信阻塞发生概率。仿真分析表明该算法能够有效地提升通信的吞吐量,降低通信的阻塞率。
英文摘要:
      In satellite communication systems, frequencies and channels are very rare resources. How to use reliable and efficient methods to develop resources has become a severe problem that needs to be solved urgently. This paper proposes a dynamic satellite channels allocation algorithm DRL DCA. This algorithm is to model satellite and environment interaction on a Markov decision making process, improving satellite decision making ability through environmental feedback, realizing efficient response to user business requests, improving the service quality of satellite communication, and reducing the probability of communication blocking. The simulation analysis shows that the proposed algorithm can effectively improve the communication throughput and reduce the communication blocking rate.
查看全文   查看/发表评论  下载PDF阅读器
关闭