在本論文中,我們提出機器學習於感知無線電網之合作式頻譜感測，我們結合了非監督式學習與監習的分類技術於頻譜感測上，在機器學習環境下，分類需要通過訓練階段分成通道可用與通道不可用。我們利用了非監督式K-means 分群演算法劃分訓練特徵向量為$K$群集，其中每個群集對應於主要使用者的狀態，接著分類器判定測試能量向量屬於的群集，將所得到之結果當作監督式高斯過程的輸入值來做分類，接著利用高斯過程來進一步提升它的效能。高斯過程分類是藉由訓練一組輸入與輸出的相關性，當再接受到新的一個輸入值時，能得到輸出的預測機率值，利用此機率值來判定新接收到的資訊為哪一類別。最後我們的模擬將比較我們提出的高斯過程分類、K-means 分群演算法與支持向量機。利用操作特性曲線來比較各方法之性能，也比較各方法之計算複雜度。

This thesis proposes the application of machine learning which combine unsupervised learning and supervised learning classification techniques for cooperative spectrum sensing(CSS) in cognitive radio networks. This method is characterized by using the results of unsupervised K-means clustering algorithm as an input of Gaussian process to determine whether the channel is available or not. The classification need to go through a training phase which K-means clustering algorithm divided the training feature vectors into K clusters, where each cluster corresponds to the state of a primary user, and then the classifier determines test energy vector belongs to. However, due to its poor classification results, we combine Gaussian process to improve its performance. Gaussian process classification is by training a group of relation about input data and output data when receiving a new input data can obtain the prediction probability of output, and use this probability to determine the new data belongs to which class. Simulation results show the operating characteristic curve(ROC) to compare the performance of each technique, and compare the computational complexity.

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