To explore the potential of machine learning-based cooperative spectrum sensing (CSS) in training time, classification speed and classification performance, this study mainly focuses on studying the problem of the feature vectors selecting for machine learning-based CSS. First, a new machine learning-based CSS framework is presented, in which, energy vector forming module, feature vector conversion module, training module, classification module and training sample database are included. Second, a new two-dimensional distance vector is developed, and it is converted by an m -dimensional energy vector according to the distance measurement between vectors. Furthermore, six combination modes are obtained by combining three feature vectors (energy, probability and distance vectors) with two supervised machine learning methods, which are support vector machine (SVM) and weighted K-nearest-neighbour, respectively. From the proposed experimental simulations, the authors can find that the distance vector is obviously superior to the probability vector in computation time. Moreover, the probability vector and distance vector are superior to the energy vector in training time except for the case of poor signal and fewer users, and obviously superior to the energy vector in classification speed. At last, the probability vector and distance vector with SVM classifier show the best classification performance in six combination modes.

中文翻译：

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通过特征向量选择提高基于机器学习的协作频谱感知的性能

为了探索基于机器学习的协作频谱感知（CSS）在训练时间，分类速度和分类性能方面的潜力，本研究主要致力于研究基于特征向量的基于机器学习的CSS选择的问题。首先，提出了一种新的基于机器学习的CSS框架，其中包括能量矢量形成模块，特征矢量转换模块，训练模块，分类模块和训练样本数据库。其次，开发了一个新的二维距离矢量，并将其转换为米 能量向量根据向量之间的距离测量。此外，通过将三个特征向量（能量，概率和距离向量）与两种有监督的机器学习方法（分别为支持向量机（SVM）和加权K最近邻）进行组合，可以获得六种组合模式。从提出的实验仿真中，作者可以发现距离向量在计算时间上明显优于概率向量。而且，除了信号差，用户少的情况外，概率向量和距离向量在训练时间上均优于能量向量，在分类速度上明显优于能量向量。最后，