说明：  基于信道的历史信息来预测不同信道的未来可用时间可以帮助CR选择一个最佳信道进行信息传递。不同的预测准则应用到周期或是随机的开关模式中。一个CR可以学习以往不同信道的模式。我们提出一个简单的分类和学习方法去检测模式的类型和收集需要的信息用于智能信道的选择。MATLAB仿真结果显示提出的方案在随机信道的随机和周期模式中都有优越的表现。随着时间的变化，新到的切换次数减少到55 ，并且延迟时间也相应减少，而吞吐量却在提高。
(Prediction of future availability times of different channels based on history information helps a cognitive radio (CR) to select the best channels for control and data transmission. Different prediction rules apply to periodic and stochastic ON-OFF patterns. A CR can learn the patterns in different channels over time. We propose a simple classification and learning method to detect the pattern type and to gather the needed information for intelligent channel selection. Matlab simulations show that the proposed method outperforms opportunistic random channel selection both with stochastic and periodic channel patterns. The amount of channel switches needed over time reduces up to 55 , which reduces also the delay and increases the throughput.)