Table 7 Comparison between different works in terms of features, ANN model and the achieved SNR with the recognition accuracy
From: Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network
Reference | Application | Applied features | Type of ANN | Recognition accuracy (%) |
|---|---|---|---|---|
[22] | AMC | Instantaneous temporal feature-based | FANN (5,19,8) and PANN (5,1800,8) | Overall success rate at -5 db are 65.63% and 55.5% respectively. |
[23] | AMC | Instantaneous temporal feature-based modulation | FANN (4,7,5) | the overall success rate at -5 dB is 99.65%. |
[25] | AMC | Continuous wavelet transform (CWT) | N/A | The overall success rate at 0 dB is 99.6% (using 10 features). |
[26] | AMC | Instantaneous information and signal spectrum | N/A | The overall success rate at 3 dB is 98.6% (using 10 features). |
[37] | AMC | Combination of the higher order moments, higher order cumulants and instantaneous characteristics of digital modulations | Radial basis function (RBF) probabilistic neural network (PNN) | The overall success rate at -3 dB is 87.50%. The overall success rate at -3 dB is 86.45%. |
[38] | AMC | 7-level DWT | Adaptive Network Based Fuzzy Inference Systems of 5 hidden layers | The overall success rate using DB2 at -5 dB is 98%. |
[39] | AMC | Haar Wavelet Transform | N/A | The overall success rate at -7 dB is 99.71%. |
[40] | AMC | Haar Wavelet Transform | N/A | The overall success rate at 5 dB is 97.93%. |
[27] | Modulation classification and signal encoding | 1-level DB2 DWT | FFNN(30,14,3) | The overall success rate a -11 dB for 3-bit glossaries is 96.0%. |
PBCCS | Modulation classification and signal encoding | 5-level DB2 DWT | FFNN(27,14,3), FFNN(27,14,4), FFNN(27,14,5) | The overall success rate at -11 dB for 3-bit, 4-bit and 5-bit glossaries are 99.0%, 90.3% and 72.79%, respectively. |