An algorithm for recursive least squares support vector machine multiuser detection based on modifying kernel
基于修改核函数的RLS－SVM多用户检测算法

To solve the problems of the complexity of SVM-MUD model and the number of support vectors, a new algorithm for nonlinear multiuser detection is proposed in the paper. The algorithm introduced the forgetting factor to get the support vectors at the first training. The number of support vectors is decreased by 28%. Then, the structure of the Riemannian geometry is introduced in the input space, and using the Riemannian geometric modifies the kernel function of the classifier and gets less improved support vectors at the second training. The algorithm simplifies the SVM-MUD model of the algorithm at the cost of only a little more bit error rate and decreases the computational complexity. Simulation results illustrate that the algorithm has an excellent effect on multipath interference suppression and shows that its performance can closely match that of the optimal detector.