用于主动频谱接入的自适应信道推荐
Adaptive Channel Recommendation For Opportunistic Spectrum Access
摘要：
我们提出了一个动态的频谱接入方案，次级用户推荐“好”的渠道，并相应地访问。我们制定的问题作为一个平均回报为基础的马尔可夫决策过程。我们证明了最优平稳频谱接入策略的存在性，并在两种渐近情形下探讨了其结构性质。由于马尔可夫决策过程的动作空间是连续的，很难通过简单地离散化动作空间并使用策略迭代、值迭代或Q学习方法来找到最优策略。相反，我们提出了一个新的算法的基础上的模型参考自适应搜索方法，并证明其收敛到最优策略。数值结果表明，在均匀信道环境和异构信道环境下，该算法比静态信道推荐算法分别提高了18%和100%，并且对信道动态具有更强的鲁棒性.
英文摘要：
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. We show the existence of the optimal stationary spectrum access policy, and explore its structure properties in two asymptotic cases. Since the action space of the Markov decision process is continuous, it is difficult to find the optimal policy by simply discretizing the action space and use the policy iteration, value iteration, or Q-learning methods. 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 algorithms achieve up to 18% and 100% performance improvement than the static channel recommendation scheme in homogeneous and heterogeneous channel environments, respectively, and is more robust to channel dynamics.
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