Recognition of digital modulation using Radial Basis Function neural networks

This paper proposes the architecture of Radial Basis Function (RBF) neural networks for the recognition of different digital modulated signals. Recursive Orthogonal Least Squares (ROLS) algorithm is used not only for calculating the weights of the network, but also for choosing RBF neural networks centers sequentially after network training, according to minimizing the output error. The final network models can achieve acceptable accuracy with significant reduction in the number of required centers without retraining. The trained networks have the ability to recognize most digital modulated signals, such as 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK and other hybrid multiplex modulated signals. The simulation results demonstrate the validity and practicability of this approach.