Functional-link models for adaptive channel equaliser

This paper presents a study of a new class of adaptive channel equaliser, known as the functional-link (FL) equaliser which utilises functional expansion of the equaliser´s input data. By carefully selecting the appropriate functions of the input, significant performance improvement can be obtained. These studies will lead to significant reduction in the exponentially increased expansion of the polynomial-perceptron equaliser. Numerical simulation results are presented to highlight the better bit error rate performance of the FL based equaliser compared to other non-linear, neural network based equalisers