Support vector machine for multiuser detection in CDMA communications
Xiaohong Gong, Anthony Kuh · 2003
We apply support vector machines (SVM) or optimal margin classifiers to multiuser detection problems. SVM are well suited for multiuser detection problems as they are based on principles of statistical learning theory where the goal is to construct a maximum margin classifier. We show that a linear SVM converges to the MMSE receiver in the noiseless case. The SVM are also modified to construct nonlinear receivers by using kernel functions and they approximate optimal nonlinear multiuser detection receivers. Using the sequential minimization optimization (SMO) algorithm, we implement SVM as receivers in CDMA systems and compare SVM with traditional and adaptive receivers. The simulation performance of SVM compares favorably to these receivers.