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In our proposed equalization scheme, we employ support vector machine (SVM) [16] to learn the required estimate using available input and output data. The SVM is a kind of kernel machine learning technique that utilizes a nonlinear mapping of the original training data [16]. It is mainly designed for binary classification, which was later extended to the multi-class problem. In supervised learning, when labeled training data is inputted, SVM outputs an optimal hyperplane, which categorizes new examples [17, 18]. The variants of SVM used in this study are Linear SVM, Quadratic SVM, and Cubic SVM [19, 20].

The main idea of the proposed equalizer is to learn the weight matrix for the FBMC equalizer via SVM using available training data {xtrain, ytrain}. Once the equalizer weight matrix is learned, the estimate for the unknown transmitted data xunknown is obtained by processing the received signal ytest through the designed SVM.