The performance comparison of discrete wavelet neural network and discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition

In this paper, a new discrete wavelet neural network (DWNN) and discrete wavelet adaptive network based fuzzy inference system (DWANFIS) methods are offered for automatic digital modulation recognition (ADMR) and the performance comparison between these new DWNN and DWANFIS intelligent systems are performed by using bior1.3, bior2.2, bior2.8, bior3.5, bior6.8, coif1, coif2, coif3, coif4, coif5, db3, db5, db8, db10, sym2, sym3, sym5, sym7, and sym8 wavelet decomposition filters, respectively. Moreover in this study, discrete wavelet transform (DWT) and adaptive wavelet entropy are used in feature extraction stages of these intelligent systems. The digital modulation types used in this study are ASK2, ASK4, ASK8, FSK2, FSK4, FSK8, PSK2, PSK4, and PSK8. Here, mean correct recognition rates for digital modulation recognition were obtained 96.51% and 90.24% by using DWNN and DWANFIS intelligent systems, respectively.