Automatic digital modulation recognition using artificial neural networks
Zhao Yaqin, Ren Guanghui, Xuexia Wang, Zhilu Wu, Xuemai Gu · 2003
This paper presents a modified structure and learning algorithm of artificial neural networks (ANN) for recognizing baseband signal modulation types in the presence of additive white Gaussian noise. The new method employs a layer with less output nodes and an error back propagation learning algorithm with momentum to improve the recognition performance. Simulation results and performance evaluation of the ANN are given and it is shown that the benefits of the developed method are that its structure is simple and it performs well at low signal to noise ratio (SNR) with high overall success rates.