Adaptive complex modified probabilistic neural network in digital channel equalization

摘要

A novel adaptive technique is proposed for the complex-valued Modified Probabilistic Neural Network (MPNN). The adaptive feature is desirable when using the MPNN in channel equalization to track time-varying channels. The MPNN is initially trained using the clustering technique. When training is completed, the network is switched to decision directed mode and the network parameters are adapted using stochastic gradient-based algorithms in an unsupervised manner. Simulations show that the equalizer was able to efficiently equalize 4-QAM symbol sequences transmitted through nonlinear, slowly time-varying channels.