Fig. 7

Classification performance comparison among FGDNN and other SoA frameworks. This figure presents the comparison result among FGDNN and five frameworks from [14,15,16,17,18], here named as LSTM-FC, CNN-LSTM2, CNN-LSTM, LSTM2, and GRU2, respectively. As of network input, CNN-LSTM2 uses both I/Q and A/P data, LSTM2 uses only A/P data as input, and the others utilize I/Q data directly. The FGDNN outperforms the other frameworks, especially at low SNRs. Specifically, FGDNN is better than CNN-LSTM2 and CNN-LSTM by 3\(\%\) and 2\(\%\) at + 10 dB SNR. This figure also shows that AMC frameworks using A/P data as input get better result in low SNR range