Using an RBF network for blind equalization: design and performance evaluation

The design of adaptive equalizers is an important topic for practical implementation of efficient digital communications. The application of a radial basis function neural network (RBF) for blind channel equalization is analysed. This architecture is well suited for equalization of finite impulse response (FIR) channels partly because the network model closely matches the data model. This allows a rather straightforward design of an optimal receiver, in a Bayesian sense. It also provides a simple framework for data classification, in which more complex nonlinear distortions can be accommodated with virtually no modifications. A clustering algorithm for dynamic creation and combination of local units is proposed, which eliminates the need for channel order estimation. An initialization procedure for the output linear layer is also presented. The network performance is illustrated with Monte Carlo simulations for a family of random channels