Nonparametric Bayesian estimation by feedforward neural networks

The paper is concerned with developing novel nonparametric detectors implemented by neural networks. These detection algorithms are of great importance in point-to-point microwave digital communication, and in mobile communication as well. It is proven that asymptotically optimal detection performance can be achieved by the proposed methods. The complexity of the newly developed algorithms is minimized by different coding techniques. Extensive numerical results demonstrate the optimal performance of the new detection schemes in the case of different channel models. This optimized algorithm enables practical real-time detection in digital communication systems.