Pattern Classification of Respective Wavelet Images With the Use of an Adaptive Multi-layer Neural Network Receiver Architecture: The KaraNetwork approach

One of the most difficult classes of signal detection problems is detecting a non-stationary signal in a non-stationary environment with unknown statistics. One of the most interesting approaches considers the use of a neural network to compute the likelihood ratio of the received signal, by training it on different realizations of the received signal. The signal detection problem is then transformed to a pattern classification problem. It is still difficult though to determine the optimum internal structure of the neural network used, in order to achieve maximum performance of the receiver with less complexity of the network. In this paper, we demonstrate the use of self-organizing neural network, the KaraNetwork. This structure optimizes performance by re-configuring its internal structure regarding on whether the generalization results are satisfactory or not. The use of KaraNetwork in the receiver architecture is also compared to a classic neural network approach of the signal detection problem.