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arXiv 0807.2677cs.NIcs.LG
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认知无线电动态频谱接入学习算法

Algorithms for Dynamic Spectrum Access with Learning for Cognitive Radio

Jayakrishnan Unnikrishnan, Venugopal Veeravalli

摘要:

本文将认知无线电系统中的动态频谱感知和接入问题作为一个部分可观测马尔可夫决策过程(POMDP)进行研究。一组认知用户合作,试图利用空缺的主要(授权)渠道,其占用遵循马尔可夫进化。我们首先考虑的情况下,认知用户有完美的知识,他们从主用户接收到的信号的分布。对于这个问题,我们得到了一个贪婪的信道选择和访问策略,最大限度地提高瞬时奖励,同时满足约束的概率干扰许可传输。我们还得到了一个分析普遍的上界的最优策略的性能。通过仿真,我们表明,我们的计划取得了良好的性能相对于上界和改进的性能相对于现有的计划。然后,我们考虑更实际的情况下,从主要的信号的确切分布是未知的。我们假设一个参数模型的分布和开发一个算法,可以学习的真实分布,仍然保证约束的干扰概率。我们表明,该算法优于天真的设计,假设一个最坏的情况下的参数值。我们还证明了学习算法的收敛性。

英文摘要:

We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperatively tries to exploit vacancies in primary (licensed) channels whose occupancies follow a Markovian evolution. We first consider the scenario where the cognitive users have perfect knowledge of the distribution of the signals they receive from the primary users. For this problem, we obtain a greedy channel selection and access policy that maximizes the instantaneous reward, while satisfying a constraint on the probability of interfering with licensed transmissions. We also derive an analytical universal upper bound on the performance of the optimal policy. Through simulation, we show that our scheme achieves good performance relative to the upper bound and improved performance relative to an existing scheme. We then consider the more practical scenario where the exact distribution of the signal from the primary is unknown. We assume a parametric model for the distribution and develop an algorithm that can learn the true distribution, still guaranteeing the constraint on the interference probability. We show that this algorithm outperforms the naive design that assumes a worst case value for the parameter. We also provide a proof for the convergence of the learning algorithm.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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