...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on mobile computing >Stochastic Power Adaptation with Multiagent Reinforcement Learning for Cognitive Wireless Mesh Networks
【24h】

Stochastic Power Adaptation with Multiagent Reinforcement Learning for Cognitive Wireless Mesh Networks

机译：认知无线网状网络中具有多主体强化学习的随机功率自适应

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

As the scarce spectrum resource is becoming overcrowded, cognitive radio indicates great flexibility to improve the spectrum efficiency by opportunistically accessing the authorized frequency bands. One of the critical challenges for operating such radios in a network is how to efficiently allocate transmission powers and frequency resource among the secondary users (SUs) while satisfying the quality-of-service constraints of the primary users. In this paper, we focus on the noncooperative power allocation problem in cognitive wireless mesh networks formed by a number of clusters with the consideration of energy efficiency. Due to the SUs' dynamic and spontaneous properties, the problem is modeled as a stochastic learning process. We first extend the single-agent $(Q)$-learning to a multiuser context, and then propose a conjecture-based multiagent $(Q)$-learning algorithm to achieve the optimal transmission strategies with only private and incomplete information. An intelligent SU performs $(Q)$-function updates based on the conjecture over the other SUs' stochastic behaviors. This learning algorithm provably converges given certain restrictions that arise during the learning procedure. Simulation experiments are used to verify the performance of our algorithm and demonstrate its effectiveness of improving the energy efficiency.
机译：随着稀缺的频谱资源变得人满为患，认知无线电显示出极大的灵活性，可以通过机会性访问授权频段来提高频谱效率。在网络中操作此类无线电的关键挑战之一是如何在满足主要用户的服务质量约束的同时，在次要用户（SU）之间高效地分配传输功率和频率资源。在本文中，我们着眼于能源效率，着重研究由多个集群组成的认知无线网格网络中的非合作功率分配问题。由于SU的动态和自发特性，该问题被建模为随机学习过程。我们首先将单代理$（Q）$学习扩展到多用户上下文，然后提出一种基于猜想的多代理$（Q）$学习算法，以仅使用私有信息和不完整信息来实现最佳传输策略。智能SU根据对其他SU的随机行为的推测执行$（Q）$功能更新。在学习过程中给定某些限制的情况下，该学习算法可证明收敛。仿真实验用于验证我们算法的性能，并证明其提高能源效率的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号