Support vector machine techniques for nonlinear equalization

The emerging machine learning technique called support vector machines is p roposed as a method for performing nonlinear equalization in communication systems. The support vector machine has the advantage that a smaller number of parameters for the model can he identified in a manner that does not re quire the extent of prior information or heuristic assumptions that some pr evious techniques require. Furthermore, the optimization method of a suppor t vector machine is quadratic programming, which is a well studied and unde rstood mathematical programming technique, Support vector machine simulations are carried out on nonlinear problems pr eviously studied by other researchers using neural networks. This allows in itial comparison against other techniques to determine the feasibility of u sing the proposed method for nonlinear detection. Results show that support vector machines perform as well as neural networks on the nonlinear proble ms investigated, A method is then proposed to introduce derision feedback processing to supp ort vector machines to address the fact that intersymbol interference (ISI) data generates input vectors having temporal correlation, whereas a standa rd support vector machine assumes independent input vectors, presenting the problem from the viewpoint of the pattern space illustrates the utility of a bank of support vector machines. This approach yields a nonlinear proces sing method that Is somewhat different than the nonlinear decision feedback method whereby the linear feedback filter of the decision feedback equaliz er is replaced by a Volterra filter. A simulation using a linear system sho ws that the proposed method performs equally to a conventional decision fee dback equalizer for this problem.