Algebraic perceptron in digital channel equalization

The paper investigates the application of the algebraic perceptron to solve the problem of channel equalization. The focus is on the particular case where the degree of intersymbol interference is severe. In recent years, some researchers have applied the support vector machine for the same application and found valuable results. However, the support vector machine requires solving a constrained optimization problem with quadratic programming, which is not a trivial task for large data sets. Like the support vector machine, the algebraic perceptron also achieves linear separation in the high dimensional feature space, but with reduced calculation requirement. The tradeoff is that the separation surface is not a maximal margin one. In the simulation, it was found that for some channels the algebraic perceptron performed better than the support vector machine. Further, given a more complete training set, the performance of the algebraic perceptron can match the performance of the support vector machine