中文摘要：

针对频谱共享中信道状态建模为完全知识马尔科夫时，应用受限的问题，提出了不同信道下基于信道感知的在线学习。根据授权用户是否存在于当前信道来选择激进发送或保守发送，由于保守发送时，信道状态是不可观测的，因此将信道模型建模为部分可观测马尔科夫决策过程。将信道未知情况下的最优传输策略建模为多臂赌博机模型。仿真结果表明，在信道不完全可知情况下的多臂赌博机在线学习算法能获得最优K步策略，并通过UCB-TUNED方法改善了最优传输的K步保守策略的收敛性。

英文摘要：

Aiming at the problems that when the spectrum sharing channel state was modeled as a complete knowledge of Mar-kov,the application was limited,different channel based on channel-aware online learning was proposed,and according to the presence or absence of authorized users,radical or conservative sending was chosen.Due to the unobservable conservative trans-mission channel state,the channel was modeled as partially observable Markov decision process （POMDP）,and the optimal transmission was modeled as multi-armed bandit in unknown channel.Results of the simulation indicated that the multi-armed bandit online learning could get the K-conservative policy in the circumstances of not fully known channel.At the same time,the convergence speed was improved by UCB-TUNED algorithm.