Support Vector based classification for Adaptive Channel Equalization

Support vector machine, a newly developed machine learning technology, is suggested as a tool for carrying out nonlinear equalization in communication networks. Support vector machine has the benefit of allowing the discovery of fewer model parameters while requiring less previous information and heuristic assumptions than some earlier systems. A support vector machine's optimization process also uses quadratic programming, a well-researched and well-understood mathematical programming paradigm.On nonlinear topics that have already been researched by other researchers utilizing neural networks, support vector machine simulations are run. This makes it possible to compare the suggested approach for nonlinear detection first to other methods in order to assess its viability. Results demonstrate that support vector machines outperform neural networks on the nonlinear issues studied.