动态频谱接入的自适应信道推荐

Xu Chen, Jianwei Huang, Husheng Li
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引用次数: 19

摘要

我们提出了一种动态频谱接入方案,辅助用户相互推荐“好的”信道并进行相应的接入。我们将这个问题表述为一个基于平均奖励的马尔可夫决策过程。由于马尔可夫决策过程的动作空间是连续的(即传输概率),简单地离散动作空间并使用策略迭代或值迭代很难找到最优策略。在此基础上,提出了一种基于模型参考自适应搜索的新算法,并证明了该算法收敛于最优策略。数值结果表明,该算法比静态信道推荐方案性能提高18%,比随机接入方案性能提高63%,并且对信道动态具有鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive channel recommendation for dynamic spectrum access
We propose a dynamic spectrum access scheme where secondary users recommend “good” channels to each other and access accordingly. We formulate the problem as an average reward based Markov decision process. Since the action space of the Markov decision process is continuous (i.e., transmission probabilities), it is difficult to find the optimal policy by simply discretizing the action space and use the policy iteration, or value iteration. Instead, we propose a new algorithm based on the Model Reference Adaptive Search method, and prove its convergence to the optimal policy. Numerical results show that the proposed algorithm achieves up to 18% performance improvement than the static channel recommendation scheme and up to 63% performance improvement than the random access scheme, and is robust to channel dynamics.
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