Least Squares Support Vector Machines for Channel Prediction in the MIMO System

A new LS-SVM method for a multiple-input multiple-output (MIMO) channel prediction is presented. A least squares support vector machine (LS-SVM) is proposed as a prediction technique. The LS-SVM has nice properties in that the algorithm implements nonlinear decision regions, converges to minimum mean squared error solutions, and can be implemented adaptively. We also formulate a recursive implementation of the LS-SVM for channel prediction in the MIMO system. The performance of the new method is shown by a simulation of the bit error rate in the given environment.