Machine learning research has largely been devoted to binary and multiclass problems relating to data mining, text categorization, and pattern/facial recognition. Recently, popular machine learning algorithms have successfully been applied to wireless communication problems, notably spread spectrum receiver design, channel equalization, and adaptive beamforming with direction of arrival estimation (DOA). Various neural network algorithms have been widely applied to these three communication topics. New advanced learning techniques, such as support vector machine (SVM) have been applied, in the binary case, to receiver design and channel equalization. This paper presents a multiclass implementation of SVMs for DOA estimation and adaptive beamforming, an important component of code division multiple access (CDMA) communication systems.
Sandia Labs; The University of New Mexico
Abdallah, Chaouki T. and Judd A. Rohwer. "Support Vector Machines for Direction of Arrival Estimation." (2012). https://digitalrepository.unm.edu/ece_fsp/36