Study of modulation recognition based on HOCs and SVM

The paper presents a new algorithm for modulation recognition of digital communication signals based on higher order cumulants (HOCs) and support vector machines (SVM). The fourth and sixth order cumulants of the received signal are used as the input classification feature vector to the SVM. The SVM maps the input vectors nonlinearly into a high dimensional feature space, constructs the optimum separating hyperplane in that space and makes the non-separable data separable. This method is robust to Gaussian noise and constellation rotation due to the initial phase of the signal and avoids the overfitting and local minimum in a neural network. The high performance and robustness of the algorithms are proved by computer simulation.